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list of schools in the bay of plenty region

https://en.wikipedia.org/wiki/List_of_schools_in_the_Bay_of_Plenty_Region

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12174210-5.html.csv

count

two of the schools are for years one through eight .

{'scope': 'all', 'criterion': 'equal', 'value': '1 - 8', 'result': '2', 'col': '2', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'years', '1 - 8'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 .', 'tostr': 'filter_eq { all_rows ; years ; 1 - 8 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; years ; 1 - 8 } }', 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; years ; 1 - 8 } } ; 2 } = true', 'tointer': 'select the rows whose years record fuzzily matches to 1 - 8 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; years ; 1 - 8 } } ; 2 } = true

select the rows whose years record fuzzily matches to 1 - 8 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'years_5': 5, '1 - 8_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'years_5': 'years', '1 - 8_6': '1 - 8', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'years_5': [0], '1 - 8_6': [0], '2_7': [2]}

['name', 'years', 'gender', 'area', 'authority', 'decile']

[['kawerau putauaki school', '1 - 8', 'coed', 'kawerau', 'state', '1'], ['kawerau south school', '1 - 6', 'coed', 'kawerau', 'state', '1'], ['kawerau teen parent unit', '-', '-', 'kawerau', 'state', '1'], ['tarawera high school', '7 - 13', 'coed', 'kawerau', 'state', '1'], ['te whata tau o putauaki', '1 - 8', 'coed', 'kawerau', 'state', '1']]

comparison of e - book readers

https://en.wikipedia.org/wiki/Comparison_of_e-book_readers

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1149661-3.html.csv

ordinal

notion ink makes the model that has the biggest screen size on the market .

{'row': '12', 'col': '4', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'screen size ( inch )', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; screen size ( inch ) ; 1 }'}, 'maker'], 'result': 'notion ink', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; screen size ( inch ) ; 1 } ; maker }'}, 'notion ink'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; screen size ( inch ) ; 1 } ; maker } ; notion ink } = true', 'tointer': 'select the row whose screen size ( inch ) record of all rows is 1st maximum . the maker record of this row is notion ink .'}

eq { hop { nth_argmax { all_rows ; screen size ( inch ) ; 1 } ; maker } ; notion ink } = true

select the row whose screen size ( inch ) record of all rows is 1st maximum . the maker record of this row is notion ink .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'screen size (inch)_5': 5, '1_6': 6, 'maker_7': 7, 'notion ink_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'screen size (inch)_5': 'screen size ( inch )', '1_6': '1', 'maker_7': 'maker', 'notion ink_8': 'notion ink'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'screen size (inch)_5': [0], '1_6': [0], 'maker_7': [1], 'notion ink_8': [2]}

['maker', 'model', 'intro year', 'screen size ( inch )', 'screen type', 'weight', 'screen pixels', 'hours reading', 'touch screen', 'wireless network', 'internal storage', 'card reader slot']

[['aluratek', 'libre touch ebook reader', '2011', '7', 'lcd', 'g ( oz )', '480 800', '8', 'yes', 'yes , wi - fi', '4 gb', 'microsd'], ['aluratek', 'libre air ebook reader', '2011', '5', 'lcd', 'g ( oz )', '480 640', '20', 'no', 'yes , wi - fi', '512 mb', 'microsd'], ['aluratek', 'libre color ebook reader', '2010', '7', 'lcd', 'g ( oz )', '480 800', '24', 'no', 'no', '2 gb', 'sd'], ['aluratek', 'libre pro ebook reader', '2009', '5', 'lcd', 'g ( oz )', '480 640', '24', 'no', 'no', '256 mb', 'sd'], ['amazoncom', 'kindle fire', '2011', '7', 'lcd ( ips )', 'g ( oz )', '600 1024', '8', 'yes', 'wi - fi', '8 gb ( 6 gb )', 'no'], ['apple inc', 'ipad ( 3rd generation )', '2012', '9.7', 'lcd ( ips )', 'g ( oz ) , g ( oz )', '2048 1536', '10', 'yes', 'wi - fi , 3 g', '16 - 64 gb', 'sd via camera connection kit'], ['apple inc', 'ipad 2', '2011', '9.7', 'lcd ( ips )', 'g ( oz )', '768 1024', '10', 'yes', 'wi - fi , 3 g', '16 - 64 gb', 'sd via camera connection kit'], ['apple inc', 'ipad', '2010', '9.7', 'lcd', 'g ( oz )', '768 1024', '9', 'yes', 'wi - fi', '16 - 64 gb', 'sd via camera connection kit'], ['barnes & noble', 'nook color', '2010', '7', 'lcd', 'g ( oz )', '600 1024', '8', 'yes', 'wi - fi 802.11 b / g / n', '2 gb , 1 gb available', 'microsdhc'], ['ectaco', 'jetbook', '2008', '5', 'lcd', 'g ( oz )', '480 640', '20', 'no', 'no', '112 mb', 'sdhc'], ['elonex', '705eb', '2010', '7', 'led', 'g ( oz )', '480 800', '8', 'no', 'no', '4 gb', 'microsdhc'], ['notion ink', 'adam', '2011', '10.1', 'pixel qi', 'g ( oz )', '600 1024', '15', 'yes', 'wi - fi , 3 g', '1 gb ddr2 ram 1 gb slc', 'microsd'], ['pocketbook', 'pocketbook iq 701', '2010', '7', 'lcd', 'g ( oz )', '600 800', '8', 'yes', 'wi - fi', '2 gb', 'sdhc'], ['trekstor', 'ebook reader 3.0', '2011', '7', 'lcd', 'g ( oz )', '800 480', '8', 'no', 'no', '2 gb', 'microsdhc'], ['zzbook', 'ereader hd', '2010', '7', 'tft - lcd', 'g ( oz )', '800 480', '8', 'no', 'no', '2 gb', 'microsd'], ['maker', 'model', 'intro year', 'screen size ( inch )', 'screen type', 'weight', 'screen pixels', 'hours reading', 'touch screen', 'wireless network', 'internal storage', 'card reader slot']]

winter garden region

https://en.wikipedia.org/wiki/Winter_Garden_Region

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10998425-1.html.csv

count

according to the winter garden region population statistics , 3 counties that had population above 1000 in 1900 had estimated population above 10,000 in 2006 .

{'scope': 'subset', 'criterion': 'greater_than', 'value': '10000', 'result': '2', 'col': '6', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '1000'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', '1900', '1000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; 1900 ; 1000 }', 'tointer': 'select the rows whose 1900 record is greater than 1000 .'}, '2006 est', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose 1900 record is greater than 1000 . among these rows , select the rows whose 2006 est record is greater than 10000 .', 'tostr': 'filter_greater { filter_greater { all_rows ; 1900 ; 1000 } ; 2006 est ; 10000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; 1900 ; 1000 } ; 2006 est ; 10000 } }', 'tointer': 'select the rows whose 1900 record is greater than 1000 . among these rows , select the rows whose 2006 est record is greater than 10000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; 1900 ; 1000 } ; 2006 est ; 10000 } } ; 2 } = true', 'tointer': 'select the rows whose 1900 record is greater than 1000 . among these rows , select the rows whose 2006 est record is greater than 10000 . the number of such rows is 2 .'}

eq { count { filter_greater { filter_greater { all_rows ; 1900 ; 1000 } ; 2006 est ; 10000 } } ; 2 } = true

select the rows whose 1900 record is greater than 1000 . among these rows , select the rows whose 2006 est record is greater than 10000 . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, '1900_6': 6, '1000_7': 7, '2006 est_8': 8, '10000_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', '1900_6': '1900', '1000_7': '1000', '2006 est_8': '2006 est', '10000_9': '10000', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], '1900_6': [0], '1000_7': [0], '2006 est_8': [1], '10000_9': [1], '2_10': [3]}

['county', '1900', '1930', '1950', '2000', '2006 est']

[['dimmit', '1106', '8828', '10654', '10248', '10385'], ['frio', '4200', '9411', '10357', '16252', '16336'], ['la salle', '2303', '8228', '7485', '5866', '5969'], ['zavala', '792', '10349', '11201', '11600', '12036'], ['total', '8401', '36816', '39697', '43966', '44726']]

list of television stations in hong kong

https://en.wikipedia.org/wiki/List_of_television_stations_in_Hong_Kong

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22274142-1.html.csv

comparative

atv world was launched earlier than tvb pearl in hong kong .

{'row_1': '4', 'row_2': '3', 'col': '6', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'channel', 'atv world'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose channel record fuzzily matches to atv world .', 'tostr': 'filter_eq { all_rows ; channel ; atv world }'}, 'launch date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; channel ; atv world } ; launch date }', 'tointer': 'select the rows whose channel record fuzzily matches to atv world . take the launch date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'channel', 'tvb pearl'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose channel record fuzzily matches to tvb pearl .', 'tostr': 'filter_eq { all_rows ; channel ; tvb pearl }'}, 'launch date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; channel ; tvb pearl } ; launch date }', 'tointer': 'select the rows whose channel record fuzzily matches to tvb pearl . take the launch date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; channel ; atv world } ; launch date } ; hop { filter_eq { all_rows ; channel ; tvb pearl } ; launch date } } = true', 'tointer': 'select the rows whose channel record fuzzily matches to atv world . take the launch date record of this row . select the rows whose channel record fuzzily matches to tvb pearl . take the launch date record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; channel ; atv world } ; launch date } ; hop { filter_eq { all_rows ; channel ; tvb pearl } ; launch date } } = true

select the rows whose channel record fuzzily matches to atv world . take the launch date record of this row . select the rows whose channel record fuzzily matches to tvb pearl . take the launch date record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'channel_7': 7, 'atv world_8': 8, 'launch date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'channel_11': 11, 'tvb pearl_12': 12, 'launch date_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'channel_7': 'channel', 'atv world_8': 'atv world', 'launch date_9': 'launch date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'channel_11': 'channel', 'tvb pearl_12': 'tvb pearl', 'launch date_13': 'launch date'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'channel_7': [0], 'atv world_8': [0], 'launch date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'channel_11': [1], 'tvb pearl_12': [1], 'launch date_13': [3]}

['ch -', 'channel', 'channel content', 'transmission', 'format', 'launch date', 'licence']

[['1 ( a ) , 81 ( d )', 'tvb jade', "tvb 's main chinese ( cantonese ) channel", 'analog & digital', 'sdtv', '19 november 1967', 'tvb'], ['2 ( a ) , 11 ( d )', 'atv home', "atv 's main chinese ( cantonese ) channel", 'analog & digital', 'sdtv', '29 may 1957', 'atv'], ['3 ( a ) , 84 ( d )', 'tvb pearl', "tvb 's main english channel", 'analog & digital', 'hdtv', '19 november 1967', 'tvb'], ['4 ( a ) , 16 ( d )', 'atv world', "atv 's main english channel", 'analog & digital', 'sdtv', '29 may 1957', 'atv'], ['32 ( d )', 'rthk 1', "rthk 's main cantonese and english channel", 'digital', 'hdtv', '9 january 2013', 'rthk'], ['83 ( d ) , 522 ( m )', 'tvb inews', 'a 24 - hour news channel broadcasting in cantonese', 'digital & mobile', 'hdtv', '11 november 2008', 'tvb']]

list of intel core i7 microprocessors

https://en.wikipedia.org/wiki/List_of_Intel_Core_i7_microprocessors

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18823880-10.html.csv

count

a total of three of the intel core i7 microprocessors have 8 mb of l3 cache .

{'scope': 'all', 'criterion': 'equal', 'value': '8 mb', 'result': '3', 'col': '7', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'l3 cache', '8 mb'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose l3 cache record fuzzily matches to 8 mb .', 'tostr': 'filter_eq { all_rows ; l3 cache ; 8 mb }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; l3 cache ; 8 mb } }', 'tointer': 'select the rows whose l3 cache record fuzzily matches to 8 mb . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; l3 cache ; 8 mb } } ; 3 } = true', 'tointer': 'select the rows whose l3 cache record fuzzily matches to 8 mb . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; l3 cache ; 8 mb } } ; 3 } = true

select the rows whose l3 cache record fuzzily matches to 8 mb . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'l3 cache_5': 5, '8 mb_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'l3 cache_5': 'l3 cache', '8 mb_6': '8 mb', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'l3 cache_5': [0], '8 mb_6': [0], '3_7': [2]}

['model number', 'sspec number', 'frequency', 'turbo', 'cores', 'l2 cache', 'l3 cache', 'i / o bus', 'mult', 'memory', 'voltage', 'tdp', 'socket', 'release date', 'part number ( s )', 'release price ( usd )']

[['core i7 - 720qm', 'slbly ( b1 )', '1.6 ghz', '1 / 1 / 6 / 9', '4', '4 256 kb', '6 mb', 'dmi', '12', '2 ddr3 - 1333', '0.65 - 1.4 v', '45 w', 'socketg1', 'september 2009', 'by80607002907ahbx80607i7720qm', '364'], ['core i7 - 740qm', 'slbqg ( b1 )', '1.73 ghz', '1 / 1 / 6 / 9', '4', '4 256 kb', '6 mb', 'dmi', '13', '2 ddr3 - 1333', '0.65 - 1.4 v', '45 w', 'socket g1', 'june 2010', 'by80607005259aabx80607i7740qm', '378'], ['core i7 - 820qm', 'slblx ( b1 )', '1.73 ghz', '1 / 1 / 8 / 10', '4', '4 256 kb', '8 mb', 'dmi', '13', '2 ddr3 - 1333', '0.65 - 1.4 v', '45 w', 'socket g1', 'september 2009', 'by80607002904ak', '546'], ['core i7 - 840qm', 'slbmp ( b1 )', '1.87 ghz', '1 / 1 / 8 / 10', '4', '4 256 kb', '8 mb', 'dmi', '14', '2 ddr3 - 1333', '0.65 - 1.4 v', '45 w', 'socket g1', 'june 2010', 'by80607002901aibx80607i7840qm', '568'], ['core i7 - 920xm', 'slblw ( b1 )', '2 ghz', '2 / 2 / 8 / 9', '4', '4 256 kb', '8 mb', 'dmi', '15', '2 ddr3 - 1333', '0.65 - 1.4 v', '55 w', 'socket g1', 'september 2009', 'by80607002529af', '1054']]

1970 - 71 california golden seals season

https://en.wikipedia.org/wiki/1970%E2%80%9371_California_Golden_Seals_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18706961-1.html.csv

count

for the 1970 - 71 california golden seals season , of the players that came from the wchl , two of them were picked after round 2 .

{'scope': 'subset', 'criterion': 'greater_than', 'value': '2', 'result': '2', 'col': '1', 'subset': {'col': '5', 'criterion': 'fuzzily_match', 'value': 'wchl'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'college / junior / club team', 'wchl'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; college / junior / club team ; wchl }', 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to wchl .'}, 'round', '2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to wchl . among these rows , select the rows whose round record is greater than 2 .', 'tostr': 'filter_greater { filter_eq { all_rows ; college / junior / club team ; wchl } ; round ; 2 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; college / junior / club team ; wchl } ; round ; 2 } }', 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to wchl . among these rows , select the rows whose round record is greater than 2 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; college / junior / club team ; wchl } ; round ; 2 } } ; 2 } = true', 'tointer': 'select the rows whose college / junior / club team record fuzzily matches to wchl . among these rows , select the rows whose round record is greater than 2 . the number of such rows is 2 .'}

eq { count { filter_greater { filter_eq { all_rows ; college / junior / club team ; wchl } ; round ; 2 } } ; 2 } = true

select the rows whose college / junior / club team record fuzzily matches to wchl . among these rows , select the rows whose round record is greater than 2 . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'college / junior / club team_6': 6, 'wchl_7': 7, 'round_8': 8, '2_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'college / junior / club team_6': 'college / junior / club team', 'wchl_7': 'wchl', 'round_8': 'round', '2_9': '2', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'college / junior / club team_6': [0], 'wchl_7': [0], 'round_8': [1], '2_9': [1], '2_10': [3]}

['round', 'pick', 'player', 'nationality', 'college / junior / club team']

[['1', '10', 'chris oddleifson', 'canada', 'winnipeg jets ( wchl )'], ['2', '19', 'pete laframboise', 'canada', "ottawa 67 's ( oha )"], ['3', '33', 'randy rota', 'canada', 'calgary centennials ( wchl )'], ['4', '47', 'ted mcaneeley', 'canada', 'edmonton oil kings ( wchl )'], ['5', '61', 'ray gibbs', 'canada', 'charlottetown ( senior )'], ['6', '75', 'doug moyes', 'canada', 'sorel black hawks ( qmjhl )'], ['7', '88', 'terry murray', 'canada', "ottawa 67 's ( oha )"], ['8', '100', 'alan henry', 'canada', 'university of north dakota ( ncaa )']]

american seafoods

https://en.wikipedia.org/wiki/American_Seafoods

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15230458-1.html.csv

superlative

of the american seafoods ' ships , the one with the highest tonnage was northern eagle .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'tonnage'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; tonnage }'}, 'name'], 'result': 'northern eagle', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; tonnage } ; name }'}, 'northern eagle'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; tonnage } ; name } ; northern eagle } = true', 'tointer': 'select the row whose tonnage record of all rows is maximum . the name record of this row is northern eagle .'}

eq { hop { argmax { all_rows ; tonnage } ; name } ; northern eagle } = true

select the row whose tonnage record of all rows is maximum . the name record of this row is northern eagle .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'tonnage_5': 5, 'name_6': 6, 'northern eagle_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'tonnage_5': 'tonnage', 'name_6': 'name', 'northern eagle_7': 'northern eagle'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'tonnage_5': [0], 'name_6': [1], 'northern eagle_7': [2]}

['name', 'length', 'tonnage', 'built by', 'year', 'engines', 'horsepowers', 'former names']

[['american dynasty', '272.0 feet', '3471', 'mangone shipyard , houston , tx', '1974', '2 , bergen diesel , brm - 8', '8000', 'artabaze , bure , sea bure'], ['american triumph', '285.0 feet', '4294', 'ls baier & co , portland , or', '1961', '2 , w채rtsil채 , 8r32d', '7939', 'acona'], ['northern jaeger', '337 feet', '3732', 'levingston shipbuilding , orange , tx', '1969', '2 , mak m453c', '6322', 'jaeger , inagua ranger ii , wisco ranger'], ['northern eagle', '344.1 feet', '4437', 'ulstein hatlo norway', '1966', '2 , bergen diesel , brm - 8', '6590', 'mauna kea , hawaiian princess'], ['northern hawk', '310.1 feet', '3732', 'brount marine corp , warren , ri', '1981', '2 , bergen diesel , brm - 8', '8790', 'state trust'], ['ocean rover', '223.0 feet', '4345', 'mcdermott shipyards , amelia , la', '1973', '3 , w채rtsil채', '7080', 'enterprise']]

bruno giacomelli

https://en.wikipedia.org/wiki/Bruno_Giacomelli

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1219697-2.html.csv

aggregation

on average , bruno giacomelli scored about 2 points per race from 1977 to 1990 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '2', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points'], 'result': '2', 'ind': 0, 'tostr': 'avg { all_rows ; points }'}, '2'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points } ; 2 } = true', 'tointer': 'the average of the points record of all rows is 2 .'}

round_eq { avg { all_rows ; points } ; 2 } = true

the average of the points record of all rows is 2 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points_4': 4, '2_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points_4': 'points', '2_5': '2'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points_4': [0], '2_5': [1]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1977', 'marlboro team mclaren', 'mclaren m23', 'ford v8', '0'], ['1978', 'marlboro team mclaren', 'mclaren m26', 'ford v8', '0'], ['1979', 'autodelta', 'alfa romeo 177', 'alfa romeo f12', '0'], ['1979', 'autodelta', 'alfa romeo 179', 'alfa romeo v12', '0'], ['1980', 'marlboro team alfa romeo', 'alfa romeo 179', 'alfa romeo v12', '4'], ['1981', 'marlboro team alfa romeo', 'alfa romeo 179c', 'alfa romeo v12', '7'], ['1981', 'marlboro team alfa romeo', 'alfa romeo 179b', 'alfa romeo v12', '7'], ['1982', 'marlboro team alfa romeo', 'alfa romeo 179d', 'alfa romeo v12', '2'], ['1982', 'marlboro team alfa romeo', 'alfa romeo 182', 'alfa romeo v12', '2'], ['1983', 'candy toleman motorsport', 'toleman tg183b', 'hart l4 t', '1'], ['1990', 'life racing engines', 'life f190', 'life w12', '0'], ['1990', 'life racing engines', 'life f190', 'judd v8', '0']]

united states house of representatives elections , 2000

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-40.html.csv

majority

a majority of those elected to the house of representatives in south carolina were republicans .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'republican', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , most of them fuzzily match to republican .', 'tostr': 'most_eq { all_rows ; party ; republican } = true'}

most_eq { all_rows ; party ; republican } = true

for the party records of all rows , most of them fuzzily match to republican .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'republican_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'republican_4': 'republican'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'republican_4': [0]}

['district', 'incumbent', 'party', 'first elected', 'results', 'candidates']

[['south carolina 1', 'mark sanford', 'republican', '1994', 'retired republican hold', 'henry brown ( r ) 60 % andy brack ( d ) 36 %'], ['south carolina 2', 'floyd spence', 'republican', '1970', 're - elected', 'floyd spence ( r ) 58 % jane frederick ( d ) 41 %'], ['south carolina 3', 'lindsey graham', 'republican', '1994', 're - elected', 'lindsey graham ( r ) 68 % george brightharp ( d ) 31 %'], ['south carolina 4', 'jim demint', 'republican', '1998', 're - elected', 'jim demint ( r ) 80 %'], ['south carolina 5', 'john spratt', 'democratic', '1982', 're - elected', 'john spratt ( d ) 59 % carl gullick ( r ) 40 %']]

seattle supersonics all - time roster

https://en.wikipedia.org/wiki/Seattle_SuperSonics_all-time_roster

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16772687-7.html.csv

superlative

jake ford is the first player to join the seattle supersonics team among those listed in the all - time roster .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '5', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'years'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; years }'}, 'player'], 'result': 'jake ford', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; years } ; player }'}, 'jake ford'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; years } ; player } ; jake ford } = true', 'tointer': 'select the row whose years record of all rows is minimum . the player record of this row is jake ford .'}

eq { hop { argmin { all_rows ; years } ; player } ; jake ford } = true

select the row whose years record of all rows is minimum . the player record of this row is jake ford .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'years_5': 5, 'player_6': 6, 'jake ford_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'years_5': 'years', 'player_6': 'player', 'jake ford_7': 'jake ford'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'years_5': [0], 'player_6': [1], 'jake ford_7': [2]}

['player', 'nationality', 'jersey number ( s )', 'position', 'years', 'from']

[['jim farmer', 'united states', '21', 'pg / sg', '1990', 'alabama'], ['noel felix', 'united states', '16', 'pf', '2006', 'fresno state'], ['al fleming', 'united states', '30', 'f', '1978', 'arizona'], ['alphonso ford', 'united states', '3', 'sg', '1994', 'mississippi valley state'], ['jake ford', 'united states', '33', 'g', '1970 - 1972', 'maryland eastern shore'], ['sherell ford', 'united states', '1', 'sf', '1995 - 1996', 'illinois ( chicago )'], ['joseph forte', 'united states', '40', 'sg', '2002 - 2003', 'north carolina'], ['danny fortson', 'united states', '21', 'pf', '2004 - 2007', 'cincinnati'], ['greg foster', 'united states', '44', 'pf / c', '1999 - 2000', 'utep'], ['jim fox', 'united states', '31', 'pf / c', '1972 - 1975', 'south carolina']]

list of true jackson , vp episodes

https://en.wikipedia.org/wiki/List_of_True_Jackson%2C_VP_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20046379-3.html.csv

unique

the episode titled ' my boss ate my homework ' of true jackson , vp was the only episode written by diana sproveri .

{'scope': 'all', 'row': '4', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'diana sproveri', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'diana sproveri'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to diana sproveri .', 'tostr': 'filter_eq { all_rows ; written by ; diana sproveri }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; written by ; diana sproveri } }', 'tointer': 'select the rows whose written by record fuzzily matches to diana sproveri . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'diana sproveri'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to diana sproveri .', 'tostr': 'filter_eq { all_rows ; written by ; diana sproveri }'}, 'title'], 'result': 'my boss ate my homework', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; written by ; diana sproveri } ; title }'}, 'my boss ate my homework'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; written by ; diana sproveri } ; title } ; my boss ate my homework }', 'tointer': 'the title record of this unqiue row is my boss ate my homework .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; written by ; diana sproveri } } ; eq { hop { filter_eq { all_rows ; written by ; diana sproveri } ; title } ; my boss ate my homework } } = true', 'tointer': 'select the rows whose written by record fuzzily matches to diana sproveri . there is only one such row in the table . the title record of this unqiue row is my boss ate my homework .'}

and { only { filter_eq { all_rows ; written by ; diana sproveri } } ; eq { hop { filter_eq { all_rows ; written by ; diana sproveri } ; title } ; my boss ate my homework } } = true

select the rows whose written by record fuzzily matches to diana sproveri . there is only one such row in the table . the title record of this unqiue row is my boss ate my homework .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'written by_7': 7, 'diana sproveri_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'my boss ate my homework_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'written by_7': 'written by', 'diana sproveri_8': 'diana sproveri', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'my boss ate my homework_10': 'my boss ate my homework'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'written by_7': [0], 'diana sproveri_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'my boss ate my homework_10': [3]}

['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )']

[['27', '1', 'true concert', 'gary halvorson', 'dan kopelman', 'november 14 , 2009', '204', '3.8'], ['30', '4', 'true parade', 'gary halvorson', 'andy gordon', 'december 12 , 2009', '210', 'n / a'], ['31', '5', 'true drama', 'roger christiansen', 'steve joe', 'january 9 , 2010', '211', '3.3'], ['32', '6', 'my boss ate my homework', 'roger christiansen', 'diana sproveri', 'january 16 , 2010', '203', 'n / a'], ['33', '7', 'little buddies', 'adam weissman', 'sib ventress', 'january 30 , 2010', '208', 'n / a'], ['34', '8', 'true valentine', 'gary halvorson', 'sebastian jones', 'february 6 , 2010', '206', 'n / a'], ['35', '9', 'true date', 'dennie gordon', 'steve joe', 'february 20 , 2010', '212', 'n / a'], ['36', '10', 'the hunky librarian', 'roger christiansen', 'sarah jane cunningham & suzie v freeman', 'march 13 , 2010', '209', 'n / a'], ['37', '11', 'saving snackleberry', 'roger christiansen', 'stacey cantwell', 'march 20 , 2010', '213', 'n / a'], ['38', '12', 'pajama party', 'gary halvorson', 'steve joe', 'april 3 , 2010', '207', 'n / a'], ['39', '13', 'the gift', 'roger christiansen', 'andy gordon', 'april 17 , 2010', '205', 'n / a'], ['40', '14', 'true royal', 'roger christiansen', 'sarah jane cunningham & suzie v freeman', 'may 1 , 2010', '214', '3.7'], ['41', '15', 'true fear', 'gary halvorson', 'sib ventress', 'may 8 , 2010', '216', 'n / a'], ['42', '16', 'the reject room', 'gary halvorson', 'dan kopelman', 'may 15 , 2010', '215', 'n / a'], ['43 - 44', '17 - 18', 'mission gone bad trapped in paris', 'gary halvorson', 'andy gordon', 'may 22 , 2010', '218 - 219', '3.4'], ['45', '19', 'heatwave', 'gregg heschong', 'steve joe', 'june 26 , 2010', '217', 'n / a']]

dancing with the stars ( u.s. season 1 )

https://en.wikipedia.org/wiki/Dancing_with_the_Stars_%28U.S._season_1%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10535354-10.html.csv

aggregation

the total score of the couple who placed safe in season 1 of dancing with the stars totaled a score of 54 .

{'scope': 'subset', 'col': '2', 'type': 'sum', 'result': '54', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'safe'}}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'safe'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; safe }', 'tointer': 'select the rows whose result record fuzzily matches to safe .'}, 'score'], 'result': '54', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; result ; safe } ; score }'}, '54'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; result ; safe } ; score } ; 54 } = true', 'tointer': 'select the rows whose result record fuzzily matches to safe . the sum of the score record of these rows is 54 .'}

round_eq { sum { filter_eq { all_rows ; result ; safe } ; score } ; 54 } = true

select the rows whose result record fuzzily matches to safe . the sum of the score record of these rows is 54 .

3

3

{'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'safe_6': 6, 'score_7': 7, '54_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'safe_6': 'safe', 'score_7': 'score', '54_8': '54'}

{'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'safe_6': [0], 'score_7': [1], '54_8': [2]}

['couple', 'score', 'dance', 'music', 'result']

[['john & charlotte', '27 ( 9 , 9 , 9 )', 'foxtrot', 'let there be love - nat king cole', 'safe'], ['john & charlotte', '27 ( 9 , 9 , 9 )', 'paso doble', 'españa cañí - mexicana aleque la - band', 'safe'], ['kelly & alec', '22 ( 8 , 7 , 7 )', 'foxtrot', "do n't know why - norah jones", 'bottom 2'], ['kelly & alec', '25 ( 9 , 8 , 8 )', 'paso doble', 'bamboleo - gipsy kings', 'bottom 2'], ['joey & ashly', '20 ( 8 , 6 , 6 )', 'foxtrot', 'big spender - shirley bassey from sweet charity', 'third place'], ['joey & ashly', '25 ( 9 , 8 , 8 )', 'paso doble', 'eye of the tiger - survivor', 'third place']]

2008 - 09 denver nuggets season

https://en.wikipedia.org/wiki/2008%E2%80%9309_Denver_Nuggets_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17355408-5.html.csv

ordinal

the denver nuggets ' games against dallas recorded their 2nd highest attendance of the 2008 - 09 season .

{'row': '6', 'col': '8', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'location attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; location attendance ; 2 }'}, 'team'], 'result': 'dallas', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; location attendance ; 2 } ; team }'}, 'dallas'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; team } ; dallas } = true', 'tointer': 'select the row whose location attendance record of all rows is 2nd maximum . the team record of this row is dallas .'}

eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; team } ; dallas } = true

select the row whose location attendance record of all rows is 2nd maximum . the team record of this row is dallas .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, '2_6': 6, 'team_7': 7, 'dallas_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', '2_6': '2', 'team_7': 'team', 'dallas_8': 'dallas'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], '2_6': [0], 'team_7': [1], 'dallas_8': [2]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['19', 'december 2', 'toronto', 'w 132 - 93 ( ot )', 'chauncey billups ( 24 )', 'nenê ( 11 )', 'chauncey billups ( 14 )', 'pepsi center 14243', '13 - 6'], ['20', 'december 4', 'san antonio', 'l 91 - 108 ( ot )', 'carmelo anthony ( 16 )', 'j r smith ( 10 )', 'j r smith , chauncey billups ( 4 )', 'pepsi center 15866', '13 - 7'], ['21', 'december 6', 'sacramento', 'w 118 - 85 ( ot )', 'chauncey billups ( 24 )', 'nenê , carmelo anthony ( 7 )', 'chauncey billups ( 4 )', 'arco arena 12322', '14 - 7'], ['22', 'december 10', 'minnesota', 'w 116 - 105 ( ot )', 'carmelo anthony ( 45 )', 'carmelo anthony ( 11 )', 'chauncey billups ( 6 )', 'pepsi center 14007', '15 - 7'], ['23', 'december 13', 'golden state', 'w 123 - 105 ( ot )', 'carmelo anthony ( 27 )', 'carmelo anthony ( 9 )', 'chauncey billups ( 11 )', 'pepsi center 15322', '16 - 7'], ['24', 'december 15', 'dallas', 'w 98 - 88 ( ot )', 'j r smith ( 25 )', 'kenyon martin ( 10 )', 'chauncey billups ( 8 )', 'american airlines center 19969', '17 - 7'], ['25', 'december 16', 'houston', 'l 96 - 108 ( ot )', 'carmelo anthony ( 22 )', 'kenyon martin ( 8 )', 'chauncey billups ( 6 )', 'toyota center 17737', '17 - 8'], ['26', 'december 19', 'cleveland', 'l 88 - 105 ( ot )', 'chauncey billups ( 16 )', 'chris andersen ( 10 )', 'anthony carter , j r smith ( 4 )', 'pepsi center 19155', '17 - 9'], ['27', 'december 20', 'phoenix', 'l 101 - 108 ( ot )', 'j r smith ( 23 )', 'nenê ( 15 )', 'chauncey billups ( 8 )', 'us airways center 18422', '17 - 10'], ['28', 'december 22', 'portland', 'w 97 - 89 ( ot )', 'chauncey billups , nenê ( 19 )', 'kenyon martin ( 12 )', 'chauncey billups ( 10 )', 'pepsi center 18611', '18 - 10'], ['29', 'december 23', 'portland', 'l 92 - 101 ( ot )', 'linas kleiza ( 20 )', 'nenê ( 13 )', 'chucky atkins ( 4 )', 'rose garden 20007', '18 - 11'], ['30', 'december 26', 'philadelphia', 'w 105 - 101 ( ot )', 'j r smith ( 27 )', 'nenê ( 12 )', 'chauncey billups ( 10 )', 'pepsi center 19155', '19 - 11'], ['31', 'december 28', 'new york', 'w 117 - 110 ( ot )', 'carmelo anthony ( 32 )', 'carmelo anthony , nenê ( 9 )', 'chauncey billups ( 5 )', 'madison square garden 19763', '20 - 11'], ['32', 'december 29', 'atlanta', 'l 91 - 109 ( ot )', 'kenyon martin ( 19 )', 'chris andersen ( 6 )', 'anthony carter ( 7 )', 'philips arena 17131', '20 - 12'], ['33', 'december 31', 'toronto', 'w 114 - 107 ( ot )', 'nenê ( 21 )', 'chris andersen ( 10 )', 'chauncey billups ( 7 )', 'air canada centre 18879', '21 - 12']]

euroleague 2007 - 08 individual statistics

https://en.wikipedia.org/wiki/Euroleague_2007%E2%80%9308_Individual_Statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16050349-4.html.csv

comparative

among the top scoring players in euroleague 2007 - 08 , jeremiah massey scored more points than kenan bajramović .

{'row_1': '2', 'row_2': '5', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'jeremiah massey'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to jeremiah massey .', 'tostr': 'filter_eq { all_rows ; name ; jeremiah massey }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; jeremiah massey } ; points }', 'tointer': 'select the rows whose name record fuzzily matches to jeremiah massey . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'kenan bajramović'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to kenan bajramović .', 'tostr': 'filter_eq { all_rows ; name ; kenan bajramović }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; kenan bajramović } ; points }', 'tointer': 'select the rows whose name record fuzzily matches to kenan bajramović . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; name ; jeremiah massey } ; points } ; hop { filter_eq { all_rows ; name ; kenan bajramović } ; points } } = true', 'tointer': 'select the rows whose name record fuzzily matches to jeremiah massey . take the points record of this row . select the rows whose name record fuzzily matches to kenan bajramović . take the points record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; name ; jeremiah massey } ; points } ; hop { filter_eq { all_rows ; name ; kenan bajramović } ; points } } = true

select the rows whose name record fuzzily matches to jeremiah massey . take the points record of this row . select the rows whose name record fuzzily matches to kenan bajramović . take the points record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'name_7': 7, 'jeremiah massey_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'kenan bajramović_12': 12, 'points_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'name_7': 'name', 'jeremiah massey_8': 'jeremiah massey', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'kenan bajramović_12': 'kenan bajramović', 'points_13': 'points'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'jeremiah massey_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'kenan bajramović_12': [1], 'points_13': [3]}

['rank', 'name', 'team', 'games', 'points']

[['1', 'will solomon', 'fenerbahçe', '6', '123'], ['2', 'jeremiah massey', 'aris thessaloniki', '6', '120'], ['3', 'lynn greer', 'olympiacos', '6', '113'], ['4', 'hollis price', 'lietuvos rytas vilnius', '6', '101'], ['4', 'kenan bajramović', 'lietuvos rytas vilnius', '6', '101']]

1972 denver broncos season

https://en.wikipedia.org/wiki/1972_Denver_Broncos_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17848578-1.html.csv

count

in the 1972 season the denver broncos played at mile high stadium 7 times .

{'scope': 'all', 'criterion': 'equal', 'value': 'mile high stadium', 'result': '7', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game site', 'mile high stadium'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium .', 'tostr': 'filter_eq { all_rows ; game site ; mile high stadium }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; game site ; mile high stadium } }', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; game site ; mile high stadium } } ; 7 } = true', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium . the number of such rows is 7 .'}

eq { count { filter_eq { all_rows ; game site ; mile high stadium } } ; 7 } = true

select the rows whose game site record fuzzily matches to mile high stadium . the number of such rows is 7 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'game site_5': 5, 'mile high stadium_6': 6, '7_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'game site_5': 'game site', 'mile high stadium_6': 'mile high stadium', '7_7': '7'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'game site_5': [0], 'mile high stadium_6': [0], '7_7': [2]}

['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance']

[['1', 'september 17', 'houston oilers', 'w 30 - 17', 'mile high stadium', '1 - 0', '51656'], ['2', 'september 24', 'san diego chargers', 'l 14 - 37', 'san diego stadium', '1 - 1', '49048'], ['3', 'october 1', 'kansas city chiefs', 'l 24 - 45', 'mile high stadium', '1 - 2', '51656'], ['4', 'october 8', 'cincinnati bengals', 'l 10 - 21', 'riverfront stadium', '1 - 3', '55812'], ['5', 'october 15', 'minnesota vikings', 'l 20 - 23', 'mile high stadium', '1 - 4', '51656'], ['6', 'october 22', 'oakland raiders', 'w 30 - 23', 'oakland - alameda county coliseum', '2 - 4', '53551'], ['7', 'october 29', 'cleveland browns', 'l 20 - 27', 'mile high stadium', '2 - 5', '51656'], ['8', 'november 5', 'new york giants', 'l 17 - 29', 'yankee stadium', '2 - 6', '62689'], ['9', 'november 12', 'los angeles rams', 'w 16 - 10', 'los angeles memorial coliseum', '3 - 6', '65398'], ['10', 'november 19', 'oakland raiders', 'l 20 - 37', 'mile high stadium', '3 - 7', '51656'], ['11', 'november 26', 'atlanta falcons', 'l 20 - 23', 'atlanta - fulton county stadium', '3 - 8', '58850'], ['12', 'december 3', 'kansas city chiefs', 'l 21 - 24', 'arrowhead stadium', '3 - 9', '66725'], ['13', 'december 10', 'san diego chargers', 'w 38 - 13', 'mile high stadium', '4 - 9', '51478'], ['14', 'december 17', 'new england patriots', 'w 45 - 21', 'mile high stadium', '5 - 9', '51656']]

philippe étancelin

https://en.wikipedia.org/wiki/Philippe_%C3%89tancelin

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235932-2.html.csv

majority

the most chassis used by philippe étancelin was talbot - lago t26c da .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'talbot-lago t26c da', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'chassis', 'talbot-lago t26c da'], 'result': True, 'ind': 0, 'tointer': 'for the chassis records of all rows , most of them fuzzily match to talbot-lago t26c da .', 'tostr': 'most_eq { all_rows ; chassis ; talbot-lago t26c da } = true'}

most_eq { all_rows ; chassis ; talbot-lago t26c da } = true

for the chassis records of all rows , most of them fuzzily match to talbot-lago t26c da .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'chassis_3': 3, 'talbot-lago t26c da_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'chassis_3': 'chassis', 'talbot-lago t26c da_4': 'talbot-lago t26c da'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'chassis_3': [0], 'talbot-lago t26c da_4': [0]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1950', 'philippe étancelin', 'talbot - lago t26c', 'talbot straight - 6', '3'], ['1950', 'automobiles talbot - darracq', 'talbot - lago t26c da', 'talbot straight - 6', '3'], ['1950', 'philippe étancelin', 'talbot - lago t26c da', 'talbot straight - 6', '3'], ['1951', 'philippe étancelin', 'talbot - lago t26c da', 'talbot straight - 6', '0'], ['1952', 'escuderia bandeirantes', 'maserati a6 gcm', 'maserati straight - 6', '0']]

fifa puskás award

https://en.wikipedia.org/wiki/FIFA_Pusk%C3%A1s_Award

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24765815-2.html.csv

majority

in the fifa puskás award voting shown the majority of players were unranked .

{'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'unranked', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'rank', 'unranked'], 'result': True, 'ind': 0, 'tointer': 'for the rank records of all rows , most of them fuzzily match to unranked .', 'tostr': 'most_eq { all_rows ; rank ; unranked } = true'}

most_eq { all_rows ; rank ; unranked } = true

for the rank records of all rows , most of them fuzzily match to unranked .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'rank_3': 3, 'unranked_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'rank_3': 'rank', 'unranked_4': 'unranked'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'rank_3': [0], 'unranked_4': [0]}

['rank', 'player', 'nationality', 'team', 'opponent', 'score', 'competition', 'vote percentage']

[['1st', 'hamit altıntop', 'turkey', 'turkey', 'kazakhstan', '0 - 2', 'uefa euro 2012 qualifying group a', '40.55 %'], ['2nd', 'linus hallenius', 'sweden', 'hammarby if', 'syrianska fc', '2 - 0', '2010 superettan', '13.23 %'], ['3rd', 'matty burrows', 'northern ireland', 'glentoran', 'portadown', '1 - 0', '2010 - 11 ifa premiership', '10.61 %'], ['unranked', 'giovanni van bronckhorst', 'netherlands', 'netherlands', 'uruguay', '1 - 0', '2010 fifa world cup semi - final', 'n / a'], ['unranked', 'lionel messi', 'argentina', 'barcelona', 'valencia', '3 - 0', '2009 - 10 la liga', 'n / a'], ['unranked', 'samir nasri', 'france', 'arsenal', 'fc porto', '5 - 0', '2009 - 10 uefa champions league knockout phase', 'n / a'], ['unranked', 'neymar', 'brazil', 'santos', 'santo andré', '2 - 1', '2010 campeonato paulista', 'n / a'], ['unranked', 'arjen robben', 'netherlands', 'bayern munich', 'schalke 04', '1 - 0', '2009 - 10 dfb - pokal semifinals', 'n / a'], ['unranked', 'siphiwe tshabalala', 'south africa', 'south africa', 'mexico', '1 - 0', '2010 fifa world cup group stage', 'n / a']]

hegang

https://en.wikipedia.org/wiki/Hegang

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1834138-2.html.csv

superlative

luobei county has the biggest population among districts and counties in hegang .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'population'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population }'}, 'english name'], 'result': 'luobei county', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population } ; english name }'}, 'luobei county'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population } ; english name } ; luobei county } = true', 'tointer': 'select the row whose population record of all rows is maximum . the english name record of this row is luobei county .'}

eq { hop { argmax { all_rows ; population } ; english name } ; luobei county } = true

select the row whose population record of all rows is maximum . the english name record of this row is luobei county .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, 'english name_6': 6, 'luobei county_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population_5': 'population', 'english name_6': 'english name', 'luobei county_7': 'luobei county'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], 'english name_6': [1], 'luobei county_7': [2]}

['english name', 'simplified', 'traditional', 'pinyin', 'area', 'population', 'density']

[['english name', 'simplified', 'traditional', 'pinyin', 'area', 'population', 'density'], ['xingshan district', '兴山区', '興山區', 'xīngshān qū', '27', '44803', '1659'], ['xiangyang district', '向阳区', '向陽區', 'xiàngyáng qū', '9', '110916', '12324'], ['gongnong district', '工农区', '工農區', 'gōngnóng qū', '11', '140070', '12734'], ['nanshan district', '南山区', '南山區', 'nánshān qū', '30', '119047', '3968'], ["xing ' an district", '兴安区', '興安區', "xīng ' ān qū", '27', '74396', '2755'], ['dongshan district', '东山区', '東山區', 'dōngshān qū', '4575', '175239', '38'], ['luobei county', '萝北县', '蘿北縣', 'luóběi xiàn', '6761', '220131', '33'], ['suibin county', '绥滨县', '綏濱縣', 'suíbīn xiàn', '3344', '174063', '52']]

golf magazine

https://en.wikipedia.org/wiki/Golf_Magazine

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11063491-1.html.csv

count

alister mackenzie designed or co-designed two of the courses .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'alister mackenzie', 'result': '2', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'designer , year', 'alister mackenzie'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose designer , year record fuzzily matches to alister mackenzie .', 'tostr': 'filter_eq { all_rows ; designer , year ; alister mackenzie }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; designer , year ; alister mackenzie } }', 'tointer': 'select the rows whose designer , year record fuzzily matches to alister mackenzie . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; designer , year ; alister mackenzie } } ; 2 } = true', 'tointer': 'select the rows whose designer , year record fuzzily matches to alister mackenzie . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; designer , year ; alister mackenzie } } ; 2 } = true

select the rows whose designer , year record fuzzily matches to alister mackenzie . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'designer , year_5': 5, 'alister mackenzie_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'designer , year_5': 'designer , year', 'alister mackenzie_6': 'alister mackenzie', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'designer , year_5': [0], 'alister mackenzie_6': [0], '2_7': [2]}

['rank', 'name', 'location', 'state', 'designer , year']

[['1', 'pine valley', 'pine valley', 'new jersey', 'george crump / harry colt , 1918'], ['2', 'cypress point', 'pebble beach', 'california', 'alister mackenzie , 1918'], ['3', 'augusta national', 'augusta', 'georgia', 'alister mackenzie / bobby jones , 1933'], ['4', 'pebble beach', 'pebble beach', 'california', 'jack neville / douglas grant , 1919'], ['5', 'shinnecock hills', 'southampton', 'new york', 'william flynn , 1931'], ['6', 'oakmont', 'oakmont', 'pennsylvania', 'henry fownes , 1903'], ['7', 'merion ( east )', 'ardmore', 'pennsylvania', 'hugh wilson , 1912'], ['8', 'sand hills', 'mullen', 'nebraska', 'bill coore / ben crenshaw , 1994'], ['9', 'pacific dunes', 'bandon', 'oregon', 'tom doak , 2001'], ['10', 'national golf links of america', 'southampton', 'new york', 'charles b macdonald , 1911']]

fibt world championships 2008

https://en.wikipedia.org/wiki/FIBT_World_Championships_2008

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13566976-7.html.csv

comparative

canada won more silver medals than the us in the 2008 fibt world championships .

{'row_1': '2', 'row_2': '3', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'canada'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record fuzzily matches to canada .', 'tostr': 'filter_eq { all_rows ; nation ; canada }'}, 'silver'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nation ; canada } ; silver }', 'tointer': 'select the rows whose nation record fuzzily matches to canada . take the silver record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'united states'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nation record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; nation ; united states }'}, 'silver'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; nation ; united states } ; silver }', 'tointer': 'select the rows whose nation record fuzzily matches to united states . take the silver record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; nation ; canada } ; silver } ; hop { filter_eq { all_rows ; nation ; united states } ; silver } } = true', 'tointer': 'select the rows whose nation record fuzzily matches to canada . take the silver record of this row . select the rows whose nation record fuzzily matches to united states . take the silver record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; nation ; canada } ; silver } ; hop { filter_eq { all_rows ; nation ; united states } ; silver } } = true

select the rows whose nation record fuzzily matches to canada . take the silver record of this row . select the rows whose nation record fuzzily matches to united states . take the silver record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nation_7': 7, 'canada_8': 8, 'silver_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'nation_11': 11, 'united states_12': 12, 'silver_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nation_7': 'nation', 'canada_8': 'canada', 'silver_9': 'silver', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'nation_11': 'nation', 'united states_12': 'united states', 'silver_13': 'silver'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'nation_7': [0], 'canada_8': [0], 'silver_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'nation_11': [1], 'united states_12': [1], 'silver_13': [3]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'germany', '5', '2', '4', '11'], ['2', 'canada', '0', '2', '0', '2'], ['3', 'united states', '0', '1', '1', '2'], ['4', 'russia', '0', '1', '1', '2'], ['5', 'united kingdom', '1', '0', '0', '1']]

2001 denver broncos season

https://en.wikipedia.org/wiki/2001_Denver_Broncos_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729083-1.html.csv

unique

the denver broncos 's week 1 game was the only one which they played against the new york giants during the 2001 season .

{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'new york giants', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'new york giants'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to new york giants .', 'tostr': 'filter_eq { all_rows ; opponent ; new york giants }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; opponent ; new york giants } }', 'tointer': 'select the rows whose opponent record fuzzily matches to new york giants . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'new york giants'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to new york giants .', 'tostr': 'filter_eq { all_rows ; opponent ; new york giants }'}, 'week'], 'result': '1', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; new york giants } ; week }'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; opponent ; new york giants } ; week } ; 1 }', 'tointer': 'the week record of this unqiue row is 1 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; opponent ; new york giants } } ; eq { hop { filter_eq { all_rows ; opponent ; new york giants } ; week } ; 1 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to new york giants . there is only one such row in the table . the week record of this unqiue row is 1 .'}

and { only { filter_eq { all_rows ; opponent ; new york giants } } ; eq { hop { filter_eq { all_rows ; opponent ; new york giants } ; week } ; 1 } } = true

select the rows whose opponent record fuzzily matches to new york giants . there is only one such row in the table . the week record of this unqiue row is 1 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'new york giants_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '1_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'new york giants_8': 'new york giants', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '1_10': '1'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'opponent_7': [0], 'new york giants_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '1_10': [3]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 10 , 2001', 'new york giants', 'w 31 - 20', '75735'], ['2', 'september 23 , 2001', 'arizona cardinals', 'w 38 - 17', '50913'], ['3', 'september 30 , 2001', 'baltimore ravens', 'l 20 - 13', '75082'], ['4', 'october 7 , 2001', 'kansas city chiefs', 'w 20 - 6', '75037'], ['5', 'october 14 , 2001', 'seattle seahawks', 'l 34 - 21', '61837'], ['6', 'october 21 , 2001', 'san diego chargers', 'l 27 - 10', '67521'], ['7', 'october 28 , 2001', 'new england patriots', 'w 31 - 20', '74750'], ['8', 'november 5 , 2001', 'oakland raiders', 'l 38 - 28', '62637'], ['9', 'november 11 , 2001', 'san diego chargers', 'w 26 - 16', '74951'], ['10', 'november 18 , 2001', 'washington redskins', 'l 17 - 10', '74622'], ['11', 'november 22 , 2001', 'dallas cowboys', 'w 26 - 24', '64104'], ['12', 'december 2 , 2001', 'miami dolphins', 'l 21 - 10', '73938'], ['13', 'december 9 , 2001', 'seattle seahawks', 'w 20 - 7', '74524'], ['14', 'december 16 , 2001', 'kansas city chiefs', 'l 26 - 23', '77778'], ['16', 'december 30 , 2001', 'oakland raiders', 'w 23 - 17', '75582'], ['17', 'january 6 , 2002', 'indianapolis colts', 'l 29 - 10', '56192']]

wru division one east

https://en.wikipedia.org/wiki/WRU_Division_One_East

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12784856-3.html.csv

comparative

in the wru division one east tredegar rfc has lost more games than newbridge rfc .

{'row_1': '12', 'row_2': '6', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'tredegar rfc'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to tredegar rfc .', 'tostr': 'filter_eq { all_rows ; club ; tredegar rfc }'}, 'lost'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; tredegar rfc } ; lost }', 'tointer': 'select the rows whose club record fuzzily matches to tredegar rfc . take the lost record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'newbridge rfc'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose club record fuzzily matches to newbridge rfc .', 'tostr': 'filter_eq { all_rows ; club ; newbridge rfc }'}, 'lost'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; club ; newbridge rfc } ; lost }', 'tointer': 'select the rows whose club record fuzzily matches to newbridge rfc . take the lost record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; club ; tredegar rfc } ; lost } ; hop { filter_eq { all_rows ; club ; newbridge rfc } ; lost } } = true', 'tointer': 'select the rows whose club record fuzzily matches to tredegar rfc . take the lost record of this row . select the rows whose club record fuzzily matches to newbridge rfc . take the lost record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; club ; tredegar rfc } ; lost } ; hop { filter_eq { all_rows ; club ; newbridge rfc } ; lost } } = true

select the rows whose club record fuzzily matches to tredegar rfc . take the lost record of this row . select the rows whose club record fuzzily matches to newbridge rfc . take the lost record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'club_7': 7, 'tredegar rfc_8': 8, 'lost_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'club_11': 11, 'newbridge rfc_12': 12, 'lost_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'club_7': 'club', 'tredegar rfc_8': 'tredegar rfc', 'lost_9': 'lost', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'club_11': 'club', 'newbridge rfc_12': 'newbridge rfc', 'lost_13': 'lost'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'club_7': [0], 'tredegar rfc_8': [0], 'lost_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'club_11': [1], 'newbridge rfc_12': [1], 'lost_13': [3]}

['club', 'played', 'drawn', 'lost', 'try bp', 'losing bp']

[['club', 'played', 'drawn', 'lost', 'try bp', 'losing bp'], ['uwic rfc', '22', '0', '3', '10', '2'], ['llanharan rfc', '22', '0', '5', '13', '3'], ['blackwood rfc', '22', '0', '6', '9', '4'], ['bargoed rfc', '22', '0', '6', '10', '2'], ['newbridge rfc', '22', '0', '9', '7', '2'], ['rumney rfc', '22', '0', '12', '5', '3'], ['bedlinog rfc', '22', '0', '13', '0', '5'], ['merthyr rfc', '22', '1', '14', '5', '5'], ['ystrad rhondda rfc', '22', '0', '15', '6', '3'], ['beddau rfc', '22', '1', '14', '3', '4'], ['tredegar rfc', '22', '0', '15', '4', '4'], ['caerphilly rfc', '22', '0', '19', '1', '5']]

calgary united f.c

https://en.wikipedia.org/wiki/Calgary_United_F.C.

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12042534-3.html.csv

aggregation

fifty two total games were played by calgary united .

{'scope': 'all', 'col': '2', 'type': 'sum', 'result': '52', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'games played'], 'result': '52', 'ind': 0, 'tostr': 'sum { all_rows ; games played }'}, '52'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; games played } ; 52 } = true', 'tointer': 'the sum of the games played record of all rows is 52 .'}

round_eq { sum { all_rows ; games played } ; 52 } = true

the sum of the games played record of all rows is 52 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'games played_4': 4, '52_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'games played_4': 'games played', '52_5': '52'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'games played_4': [0], '52_5': [1]}

['team', 'games played', 'wins', 'losses', 'winning percentage', 'points for', 'points against', 'point differential']

[['2007', '4', '2', '2', '500', '9', '6', '+ 3'], ['2008', '10', '8', '2', '800', '72', '38', '+ 34'], ['2009', '16', '8', '8', '500', '109', '84', '+ 21'], ['2010', '10', '8', '2', '800', '79', '32', '+ 47'], ['2011', '12', '8', '4', '667', '68', '52', '+ 16']]

umberto maglioli

https://en.wikipedia.org/wiki/Umberto_Maglioli

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235883-1.html.csv

comparative

umberto maglioli scored more points in 1954 than he did in 1957 .

{'row_1': '3', 'row_2': '8', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1954'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1954 .', 'tostr': 'filter_eq { all_rows ; year ; 1954 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1954 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1954 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1957'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1957 .', 'tostr': 'filter_eq { all_rows ; year ; 1957 }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1957 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1957 . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1954 } ; points } ; hop { filter_eq { all_rows ; year ; 1957 } ; points } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1954 . take the points record of this row . select the rows whose year record fuzzily matches to 1957 . take the points record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; year ; 1954 } ; points } ; hop { filter_eq { all_rows ; year ; 1957 } ; points } } = true

select the rows whose year record fuzzily matches to 1954 . take the points record of this row . select the rows whose year record fuzzily matches to 1957 . take the points record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1954_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1957_12': 12, 'points_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1954_8': '1954', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1957_12': '1957', 'points_13': 'points'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1954_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1957_12': [1], 'points_13': [3]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1953', 'scuderia ferrari', 'ferrari 553', 'ferrari straight - 4', '0'], ['1954', 'scuderia ferrari', 'ferrari 625', 'ferrari straight - 4', '2'], ['1954', 'scuderia ferrari', 'ferrari 553', 'ferrari straight - 4', '2'], ['1955', 'scuderia ferrari', 'ferrari 625', 'ferrari straight - 4', '1\xa01⁄3'], ['1955', 'scuderia ferrari', 'ferrari 555', 'ferrari straight - 4', '1\xa01⁄3'], ['1956', 'scuderia guastalla', 'maserati 250f', 'maserati straight - 6', '0'], ['1956', 'officine alfieri maserati', 'maserati 250f', 'maserati straight - 6', '0'], ['1957', 'dr ing f porsche kg', 'porsche 550rs f2', 'porsche flat - 4', '0']]

list of how it 's made episodes

https://en.wikipedia.org/wiki/List_of_How_It%27s_Made_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15187735-20.html.csv

unique

episode 259 of how it 's made series 20 is the only one of that series with a two part segment .

{'scope': 'all', 'row': '12', 'col': '5', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'part', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment c', 'part'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment c record fuzzily matches to part .', 'tostr': 'filter_eq { all_rows ; segment c ; part }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; segment c ; part } }', 'tointer': 'select the rows whose segment c record fuzzily matches to part . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'segment c', 'part'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose segment c record fuzzily matches to part .', 'tostr': 'filter_eq { all_rows ; segment c ; part }'}, 'episode'], 'result': '259', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; segment c ; part } ; episode }'}, '259'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; segment c ; part } ; episode } ; 259 }', 'tointer': 'the episode record of this unqiue row is 259 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; segment c ; part } } ; eq { hop { filter_eq { all_rows ; segment c ; part } ; episode } ; 259 } } = true', 'tointer': 'select the rows whose segment c record fuzzily matches to part . there is only one such row in the table . the episode record of this unqiue row is 259 .'}

and { only { filter_eq { all_rows ; segment c ; part } } ; eq { hop { filter_eq { all_rows ; segment c ; part } ; episode } ; 259 } } = true

select the rows whose segment c record fuzzily matches to part . there is only one such row in the table . the episode record of this unqiue row is 259 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'segment c_7': 7, 'part_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'episode_9': 9, '259_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'segment c_7': 'segment c', 'part_8': 'part', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'episode_9': 'episode', '259_10': '259'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'segment c_7': [0], 'part_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'episode_9': [2], '259_10': [3]}

['series ep', 'episode', 'segment a', 'segment b', 'segment c', 'segment d']

[['20 - 01', '248', 'native healing drums', 's raisin', 'stereoscopic viewers', 'ribbon microphones'], ['20 - 02', '249', 'horse bits', 'oat cereal', 'turquoise jewellery', 'electric scooters'], ['20 - 03', '250', 'nail nippers', 'jade putters', 'ice cider', 'water skis'], ['20 - 04', '251', 'es stagecoach', 'road reflectors', 'fire baked pottery', 'custom motorcycle tanks'], ['20 - 05', '252', 'replica clay pipes', 'drinking fountains', 'orange liqueur', 'compound bows'], ['20 - 06', '253', 'tissues', 'travel trailers', 's slipper', 'motorcycle helmets'], ['20 - 07', '254', 'u - locks', 'tepees', 's croissant', 'rolling luggage'], ['20 - 08', '255', 'prams', 'factory - built homes', 'wood flutes', 'bicycle tires'], ['20 - 09', '256', 'thinning shears', 'wagon wheels', 'toaster pastries', 'violin bows'], ['20 - 10', '257', 'cast iron tubs', 'hopi kachina dolls', 'mine truck engine rebuild', 'memory cards'], ['20 - 11', '258', 'cycling shoes', 's yurt', 'marine plywood', 'oil & encaustic paint'], ['20 - 12', '259', 'paper fans', 'walnut oil', 'copper ( part 1 )', 'copper ( part 2 )']]

2007 saskatchewan roughriders season

https://en.wikipedia.org/wiki/2007_Saskatchewan_Roughriders_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16945617-4.html.csv

aggregation

the average crowd attendance for games in the 2007 saskatchewan roughriders season was 30349 .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '30349', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '30349', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '30349'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 30349 } = true', 'tointer': 'the average of the attendance record of all rows is 30349 .'}

round_eq { avg { all_rows ; attendance } ; 30349 } = true

the average of the attendance record of all rows is 30349 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '30349_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '30349_5': '30349'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '30349_5': [1]}

['week', 'date', 'opponent', 'score', 'result', 'attendance', 'record']

[['1', 'fri , june 29', 'montreal alouettes', '16 - 7', 'win', '20202', '1 - 0'], ['2', 'sun , july 8', 'calgary stampeders', '49 - 8', 'win', '25862', '2 - 0'], ['3', 'fri , july 13', 'bc lions', '42 - 12', 'loss', '26981', '2 - 1'], ['4', 'fri , july 20', 'edmonton eskimos', '21 - 20', 'loss', '46704', '2 - 2'], ['5', 'sat , july 28', 'edmonton eskimos', '54 - 14', 'win', '26840', '3 - 2'], ['6', 'thurs , aug 2', 'bc lions', '21 - 9', 'win', '31858', '4 - 2'], ['7', 'fri , aug 10', 'toronto argonauts', '24 - 13', 'win', '34234', '5 - 2'], ['8', 'sat , aug 18', 'edmonton eskimos', '39 - 32', 'win', '28800', '6 - 2'], ['9', '-', '-', '-', '-', '-', ''], ['10', 'sun , sept 2', 'winnipeg blue bombers', '31 - 26', 'win', '28800', '7 - 2'], ['11', 'sun , sept 9', 'winnipeg blue bombers', '34 - 15', 'loss', '29783', '7 - 3'], ['12', 'sat , sept 15', 'calgary stampeders', '44 - 22', 'loss', '35650', '7 - 4'], ['13', 'sat , sept 22', 'bc lions', '37 - 34', 'loss', '28800', '7 - 5'], ['14', 'sat , sept 29', 'montreal alouettes', '33 - 22', 'win', '28800', '8 - 5'], ['15', 'mon , oct 8', 'calgary stampeders', '33 - 21', 'win', '33075', '9 - 5'], ['16', 'sun , oct 14', 'hamilton tiger cats', '40 - 23', 'win', '22167', '10 - 5'], ['17', 'sun , oct 21', 'hamilton tiger cats', '38 - 11', 'win', '28800', '11 - 5'], ['18', 'fri , oct 26', 'edmonton eskimos', '36 - 29 ( ot )', 'win', '40127', '12 - 5'], ['19', 'sat , nov 3', 'toronto argonauts', '41 - 13', 'loss', '28800', '12 - 6']]

1959 vfl season

https://en.wikipedia.org/wiki/1959_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10775038-15.html.csv

aggregation

in the 1959 vfl season , melbourne teams averaged 15,750 spectators at home games .

{'scope': 'subset', 'col': '6', 'type': 'average', 'result': '15750', 'subset': {'col': '1', 'criterion': 'fuzzily_match', 'value': 'melbourne'}}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'melbourne'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home team ; melbourne }', 'tointer': 'select the rows whose home team record fuzzily matches to melbourne .'}, 'crowd'], 'result': '15750', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; home team ; melbourne } ; crowd }'}, '15750'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; home team ; melbourne } ; crowd } ; 15750 } = true', 'tointer': 'select the rows whose home team record fuzzily matches to melbourne . the average of the crowd record of these rows is 15750 .'}

round_eq { avg { filter_eq { all_rows ; home team ; melbourne } ; crowd } ; 15750 } = true

select the rows whose home team record fuzzily matches to melbourne . the average of the crowd record of these rows is 15750 .

3

3

{'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'home team_5': 5, 'melbourne_6': 6, 'crowd_7': 7, '15750_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'home team_5': 'home team', 'melbourne_6': 'melbourne', 'crowd_7': 'crowd', '15750_8': '15750'}

{'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'home team_5': [0], 'melbourne_6': [0], 'crowd_7': [1], '15750_8': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['footscray', '12.8 ( 80 )', 'melbourne', '10.11 ( 71 )', 'western oval', '12549', '8 august 1959'], ['fitzroy', '11.15 ( 81 )', 'geelong', '6.11 ( 47 )', 'brunswick street oval', '14488', '8 august 1959'], ['collingwood', '12.18 ( 90 )', 'st kilda', '9.11 ( 65 )', 'victoria park', '29178', '8 august 1959'], ['south melbourne', '8.14 ( 62 )', 'hawthorn', '10.3 ( 63 )', 'lake oval', '13000', '8 august 1959'], ['north melbourne', '11.8 ( 74 )', 'essendon', '16.14 ( 110 )', 'arden street oval', '18500', '8 august 1959'], ['richmond', '13.6 ( 84 )', 'carlton', '14.20 ( 104 )', 'punt road oval', '16000', '8 august 1959']]

memphis grizzlies all - time roster

https://en.wikipedia.org/wiki/Memphis_Grizzlies_all-time_roster

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16494599-3.html.csv

unique

of these players , only pete chilcutt had the position " power forward . " .

{'scope': 'all', 'row': '5', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'power forward', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'power forward'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to power forward .', 'tostr': 'filter_eq { all_rows ; position ; power forward }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; power forward } }', 'tointer': 'select the rows whose position record fuzzily matches to power forward . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'power forward'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to power forward .', 'tostr': 'filter_eq { all_rows ; position ; power forward }'}, 'player'], 'result': 'pete chilcutt', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; power forward } ; player }'}, 'pete chilcutt'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; power forward } ; player } ; pete chilcutt }', 'tointer': 'the player record of this unqiue row is pete chilcutt .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; position ; power forward } } ; eq { hop { filter_eq { all_rows ; position ; power forward } ; player } ; pete chilcutt } } = true', 'tointer': 'select the rows whose position record fuzzily matches to power forward . there is only one such row in the table . the player record of this unqiue row is pete chilcutt .'}

and { only { filter_eq { all_rows ; position ; power forward } } ; eq { hop { filter_eq { all_rows ; position ; power forward } ; player } ; pete chilcutt } } = true

select the rows whose position record fuzzily matches to power forward . there is only one such row in the table . the player record of this unqiue row is pete chilcutt .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'position_7': 7, 'power forward_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'pete chilcutt_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'position_7': 'position', 'power forward_8': 'power forward', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'pete chilcutt_10': 'pete chilcutt'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'position_7': [0], 'power forward_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'pete chilcutt_10': [3]}

['player', 'nationality', 'position', 'years for grizzlies', 'school / club team']

[['brian cardinal', 'united states', 'forward', '2004 - 2008', 'purdue'], ['rodney carney', 'united states', 'forward', '2011', 'memphis'], ['antoine carr', 'united states', 'forward / center', '1999 - 2000', 'wichita state'], ['demarre carroll', 'united states', 'forward', '2009 - 2012', 'missouri'], ['pete chilcutt', 'united states', 'power forward', '1996 - 1999', 'north carolina'], ['jason collins', 'united states', 'center', '2008', 'stanford'], ['mike conley , jr', 'united states', 'point guard', '2007present', 'ohio state'], ['will conroy', 'united states', 'guard', '2007', 'washington'], ['javaris crittenton', 'united states', 'point guard', '2008', 'georgia tech'], ['dante cunningham', 'united states', 'forward', '2011 - 2012', 'villanova']]

2009 russian professional rugby league season

https://en.wikipedia.org/wiki/2009_Russian_Professional_Rugby_League_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27536877-1.html.csv

count

in the 2009 russan professional rugby league , 2 teams won exactly 9 games .

{'scope': 'all', 'criterion': 'equal', 'value': '9', 'result': '2', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'won', '9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose won record is equal to 9 .', 'tostr': 'filter_eq { all_rows ; won ; 9 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; won ; 9 } }', 'tointer': 'select the rows whose won record is equal to 9 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; won ; 9 } } ; 2 } = true', 'tointer': 'select the rows whose won record is equal to 9 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; won ; 9 } } ; 2 } = true

select the rows whose won record is equal to 9 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'won_5': 5, '9_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'won_5': 'won', '9_6': '9', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'won_5': [0], '9_6': [0], '2_7': [2]}

['', 'nation', 'played', 'won', 'drawn', 'lost', 'for', 'against', 'difference', 'table points']

[['1', 'vva - podmoskovye monino', '10', '9', '0', '1', '399', '128', '271', '37'], ['2', 'yenisey - stm krasnoyarsk', '10', '9', '0', '1', '331', '140', '191', '37'], ['3', 'krasny yar krasnoyarsk', '10', '6', '0', '4', '247', '198', '49', '28'], ['4', 'slava moscow', '10', '3', '0', '7', '126', '267', '- 141', '19'], ['5', 'imperia - dynamo penza', '10', '2', '0', '8', '130', '284', '- 154', '16']]

2008 tim hortons brier

https://en.wikipedia.org/wiki/2008_Tim_Hortons_Brier

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15597975-2.html.csv

aggregation

the participants in the 2008 tim hortons brier curling tournament had a total combined ends won of 516 .

{'scope': 'all', 'col': '7', 'type': 'sum', 'result': '516', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'ends won'], 'result': '516', 'ind': 0, 'tostr': 'sum { all_rows ; ends won }'}, '516'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; ends won } ; 516 } = true', 'tointer': 'the sum of the ends won record of all rows is 516 .'}

round_eq { sum { all_rows ; ends won } ; 516 } = true

the sum of the ends won record of all rows is 516 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'ends won_4': 4, '516_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'ends won_4': 'ends won', '516_5': '516'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'ends won_4': [0], '516_5': [1]}

['locale', 'skip', 'w', 'l', 'pf', 'pa', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot pct']

[['alberta', 'kevin martin', '11', '0', '86', '52', '50', '40', '11', '11', '89'], ['saskatchewan', 'pat simmons', '9', '2', '80', '58', '50', '45', '9', '12', '84'], ['ontario', 'glenn howard', '9', '2', '85', '50', '54', '33', '11', '22', '88'], ['british columbia', 'bob ursel', '7', '4', '72', '66', '45', '47', '15', '11', '84'], ['newfoundland and labrador', 'brad gushue', '7', '4', '77', '69', '51', '44', '13', '14', '82'], ['manitoba', 'kerry burtnyk', '6', '5', '59', '66', '47', '40', '2', '19', '79'], ['quebec', 'jean - michel mãnard', '4', '7', '76', '69', '48', '48', '11', '15', '80'], ['northern ontario', 'eric harnden', '3', '8', '65', '80', '43', '53', '6', '6', '79'], ['prince edward island', 'peter gallant', '3', '8', '61', '78', '40', '50', '6', '7', '77'], ['nova scotia', 'brian rafuse', '3', '8', '60', '92', '42', '56', '8', '3', '77'], ['new brunswick', 'james grattan', '2', '9', '71', '86', '46', '54', '7', '5', '79']]

2008 manx grand prix

https://en.wikipedia.org/wiki/2008_Manx_Grand_Prix

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18649514-4.html.csv

comparative

wattie brown finished with a better time than chris swallow in the 2008 manx grand prix .

{'row_1': '4', 'row_2': '8', 'col': '5', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rider', 'wattie brown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rider record fuzzily matches to wattie brown .', 'tostr': 'filter_eq { all_rows ; rider ; wattie brown }'}, 'time'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; rider ; wattie brown } ; time }', 'tointer': 'select the rows whose rider record fuzzily matches to wattie brown . take the time record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'rider', 'chris swallow'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose rider record fuzzily matches to chris swallow .', 'tostr': 'filter_eq { all_rows ; rider ; chris swallow }'}, 'time'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; rider ; chris swallow } ; time }', 'tointer': 'select the rows whose rider record fuzzily matches to chris swallow . take the time record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; rider ; wattie brown } ; time } ; hop { filter_eq { all_rows ; rider ; chris swallow } ; time } } = true', 'tointer': 'select the rows whose rider record fuzzily matches to wattie brown . take the time record of this row . select the rows whose rider record fuzzily matches to chris swallow . take the time record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; rider ; wattie brown } ; time } ; hop { filter_eq { all_rows ; rider ; chris swallow } ; time } } = true

select the rows whose rider record fuzzily matches to wattie brown . take the time record of this row . select the rows whose rider record fuzzily matches to chris swallow . take the time record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'rider_7': 7, 'wattie brown_8': 8, 'time_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'rider_11': 11, 'chris swallow_12': 12, 'time_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'rider_7': 'rider', 'wattie brown_8': 'wattie brown', 'time_9': 'time', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'rider_11': 'rider', 'chris swallow_12': 'chris swallow', 'time_13': 'time'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'rider_7': [0], 'wattie brown_8': [0], 'time_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'rider_11': [1], 'chris swallow_12': [1], 'time_13': [3]}

['rank', 'rider', 'team', 'speed', 'time']

[['1', 'ryan farquhar', '498cc bic paton', '102.385 mph', '1:06.19.90'], ['2', 'alan oversby', '500cc norton manx', '101.863 mph', '1:06.40.30'], ['3', 'alan brew', 'seeley g50 496cc', '99.367 mph', '1:08.20.78'], ['4', 'wattie brown', '500cc petty manx', '98.118 mph', '1:09.12.98'], ['5', 'andy reynolds', '499cc bic paton', '97.152 mph', '1:09.54.28'], ['6', 'bob price', '500cc seeley g50', '96.890 mph', '1:10.05.64'], ['7', 'ken davis', '500cc norton manx', '95.948 mph', '1:10.46.92'], ['8', 'chris swallow', '476cc ducati', '95.664 mph', '1:10.59.52'], ['9', 'mark herbertson', '499cc matchless g50', '95.272 mph', '1:11.17.05'], ['10', 'dave madsen - mygdal', '499cc honda', '92.209 mph', '1:11.19.89']]

gulf coast athletic conference

https://en.wikipedia.org/wiki/Gulf_Coast_Athletic_Conference

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10577579-2.html.csv

aggregation

the gulf course had a total 11100 enrollment between 1981 and 2011 .

{'scope': 'all', 'col': '7', 'type': 'sum', 'result': '11100', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'enrollment'], 'result': '11100', 'ind': 0, 'tostr': 'sum { all_rows ; enrollment }'}, '11100'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; enrollment } ; 11100 } = true', 'tointer': 'the sum of the enrollment record of all rows is 11100 .'}

round_eq { sum { all_rows ; enrollment } ; 11100 } = true

the sum of the enrollment record of all rows is 11100 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'enrollment_4': 4, '11100_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'enrollment_4': 'enrollment', '11100_5': '11100'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'enrollment_4': [0], '11100_5': [1]}

['institution', 'location', "men 's nickname", "women 's nickname", 'founded', 'type', 'enrollment', 'joined']

[['dillard university', 'new orleans , louisiana', 'bleu devils', 'lady bleu devils', '1869', 'private / ( methodist & church of christ )', '900', '1981'], ['edward waters college', 'jacksonville , florida', 'tigers', 'lady tigers', '1866', 'private / ( african methodist )', '800', '2010'], ['fisk university', 'nashville , tennessee', 'bulldogs', 'lady bulldogs', '1866', 'private / ( church of christ )', '800', '2010'], ['philander smith college', 'little rock , arkansas', 'panthers', 'lady panthers', '1864', 'private / ( methodist )', '700', '2011'], ['southern university at new orleans', 'new orleans , louisiana', 'black knights', 'lady knights', '1956', 'public', '3200', '1986'], ['talladega college', 'talladega , alabama', 'tornadoes', 'lady tornadoes', '1867', 'private / ( united church of christ )', '600', '1999 , 2011'], ['tougaloo college', 'tougaloo , mississippi', 'bulldogs', 'lady bulldogs', '1869', 'private / ( church of christ )', '900', '1981'], ['xavier university of louisiana', 'new orleans , louisiana', 'gold rush', 'gold nuggets', '1925', 'private / ( catholic )', '3200', '1981']]

administrative divisions of lithuania

https://en.wikipedia.org/wiki/Administrative_divisions_of_Lithuania

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1784514-1.html.csv

aggregation

the average number of powiats for the administrative divisions of lithuania is 2.5 powiats .

{'scope': 'all', 'col': '4', 'type': 'average', 'result': '2.5', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'number of powiats'], 'result': '2.5', 'ind': 0, 'tostr': 'avg { all_rows ; number of powiats }'}, '2.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; number of powiats } ; 2.5 } = true', 'tointer': 'the average of the number of powiats record of all rows is 2.5 .'}

round_eq { avg { all_rows ; number of powiats } ; 2.5 } = true

the average of the number of powiats record of all rows is 2.5 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'number of powiats_4': 4, '2.5_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'number of powiats_4': 'number of powiats', '2.5_5': '2.5'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'number of powiats_4': [0], '2.5_5': [1]}

['voivodeship after 1569', 'capital', 'year established', 'number of powiats', 'area ( km square ) in 1590 ( lithuanian ) category : articles with lithuanian - language external links']

[['brest litovsk voivodeship', 'brest', '1566', '2 powiats', '40600'], ['minsk voivodeship', 'minsk', '1566', '3 powiats', '55500'], ['mstsislaw voivodeship', 'mstsislaw', '1566', '1 powiat', '22600'], ['nowogródek voivodeship', 'navahrudak', '1507', '3 powiats', '33200'], ['polotsk voivodeship', 'polotsk', '1504', '1 powiat', '21800'], ['samogitian eldership', 'raseiniai', '1411', '1 powiat', '23300'], ['trakai voivodeship', 'trakai', '1413', '4 powiats', '31100'], ['vilnius voivodeship', 'vilnius', '1413', '5 powiats', '44200']]

2004 cleveland browns season

https://en.wikipedia.org/wiki/2004_Cleveland_Browns_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10652530-2.html.csv

unique

during the 2004 season , the cleveland browns game in week 15 was the only one in which they failed to score any points .

{'scope': 'all', 'row': '15', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'result', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; result ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; 0 } }', 'tointer': 'select the rows whose result record is equal to 0 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'result', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; result ; 0 }'}, 'week'], 'result': '15', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; 0 } ; week }'}, '15'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; 0 } ; week } ; 15 }', 'tointer': 'the week record of this unqiue row is 15 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; 0 } } ; eq { hop { filter_eq { all_rows ; result ; 0 } ; week } ; 15 } } = true', 'tointer': 'select the rows whose result record is equal to 0 . there is only one such row in the table . the week record of this unqiue row is 15 .'}

and { only { filter_eq { all_rows ; result ; 0 } } ; eq { hop { filter_eq { all_rows ; result ; 0 } ; week } ; 15 } } = true

select the rows whose result record is equal to 0 . there is only one such row in the table . the week record of this unqiue row is 15 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, '0_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'week_9': 9, '15_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', '0_8': '0', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'week_9': 'week', '15_10': '15'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], '0_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'week_9': [2], '15_10': [3]}

['week', 'date', 'opponent', 'result', 'stadium', 'record', 'attendance']

[['1', 'september 12 , 2004', 'baltimore ravens', 'w 20 - 3', 'cleveland browns stadium', '1 - 0', '73068'], ['2', 'september 19 , 2004', 'dallas cowboys', 'l 12 - 19', 'texas stadium', '1 - 1', '63119'], ['3', 'september 26 , 2004', 'new york giants', 'l 10 - 27', 'giants stadium', '1 - 2', '78521'], ['4', 'october 3 , 2004', 'washington redskins', 'w 17 - 13', 'cleveland browns stadium', '2 - 2', '73348'], ['5', 'october 10 , 2004', 'pittsburgh steelers', 'l 23 - 34', 'heinz field', '2 - 3', '63609'], ['6', 'october 17 , 2004', 'cincinnati bengals', 'w 34 - 17', 'cleveland browns stadium', '3 - 3', '73263'], ['7', 'october 24 , 2004', 'philadelphia eagles', 'l 31 - 34', 'cleveland browns stadium', '3 - 4', '73394'], ['8', '-', '-', '-', '-', '-', ''], ['9', 'november 7 , 2004', 'baltimore ravens', 'l 13 - 27', 'm & t bank stadium', '3 - 5', '69781'], ['10', 'november 14 , 2004', 'pittsburgh steelers', 'l 10 - 24', 'cleveland browns stadium', '3 - 6', '73703'], ['11', 'november 21 , 2004', 'new york jets', 'l 7 - 10', 'cleveland browns stadium', '3 - 7', '72547'], ['12', 'november 28 , 2004', 'cincinnati bengals', 'l 48 - 58', 'paul brown stadium', '3 - 8', '65677'], ['13', 'december 5 , 2004', 'new england patriots', 'l 15 - 42', 'cleveland browns stadium', '3 - 9', '73028'], ['14', 'december 12 , 2004', 'buffalo bills', 'l 7 - 37', 'ralph wilson stadium', '3 - 10', '72330'], ['15', 'december 19 , 2004', 'san diego chargers', 'l 0 - 21', 'cleveland browns stadium', '3 - 11', '72489'], ['16', 'december 26 , 2004', 'miami dolphins', 'l 7 - 10', 'pro player stadium', '3 - 12', '73169'], ['17', 'january 2 , 2005', 'houston texans', 'w 22 - 14', 'reliant stadium', '4 - 12', '70724']]

2008 - 09 golden state warriors season

https://en.wikipedia.org/wiki/2008%E2%80%9309_Golden_State_Warriors_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17080868-7.html.csv

majority

most of the players had high points of above 20 points in the golden state warriors of 2008-09 .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '20', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'high points', '20'], 'result': True, 'ind': 0, 'tointer': 'for the high points records of all rows , most of them are greater than 20 .', 'tostr': 'most_greater { all_rows ; high points ; 20 } = true'}

most_greater { all_rows ; high points ; 20 } = true

for the high points records of all rows , most of them are greater than 20 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high points_3': 3, '20_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high points_3': 'high points', '20_4': '20'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'high points_3': [0], '20_4': [0]}

['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record']

[['35', 'january 2', 'minnesota', 'l 108 - 115 ( ot )', 'stephen jackson ( 25 )', 'andris biedriņš ( 13 )', 'stephen jackson ( 7 )', 'target center 11921', '10 - 25'], ['36', 'january 5', 'utah', 'l 114 - 119 ( ot )', 'jamal crawford ( 28 )', 'andris biedriņš ( 17 )', 'jamal crawford ( 6 )', 'energysolutions arena 19911', '10 - 26'], ['37', 'january 7', 'la lakers', 'l 106 - 114 ( ot )', 'jamal crawford ( 25 )', 'andris biedriņš ( 17 )', 'jamal crawford ( 9 )', 'oracle arena 19596', '10 - 27'], ['38', 'january 10', 'portland', 'l 100 - 113 ( ot )', 'corey maggette ( 25 )', 'andris biedriņš , ronny turiaf ( 6 )', 'ronny turiaf ( 7 )', 'rose garden 20687', '10 - 28'], ['39', 'january 11', 'indiana', 'w 120 - 117 ( ot )', 'jamal crawford ( 32 )', 'andris biedriņš ( 9 )', 'jamal crawford , c j watson , ronny turiaf ( 5 )', 'oracle arena 18262', '11 - 28'], ['40', 'january 14', 'sacramento', 'l 133 - 135 ( 3ot )', 'jamal crawford ( 35 )', 'andris biedriņš ( 14 )', 'c j watson ( 6 )', 'oracle arena 19122', '11 - 29'], ['41', 'january 16', 'atlanta', 'w 119 - 114 ( ot )', 'jamal crawford ( 29 )', 'corey maggette ( 16 )', 'stephen jackson ( 6 )', 'oracle arena 18832', '12 - 29'], ['42', 'january 19', 'washington', 'w 119 - 98 ( ot )', 'jamal crawford ( 28 )', 'andris biedriņš ( 15 )', 'jamal crawford ( 8 )', 'oracle arena 19244', '13 - 29'], ['43', 'january 21', 'oklahoma city', 'l 121 - 122 ( ot )', 'stephen jackson ( 29 )', 'jamal crawford ( 7 )', 'ronny turiaf ( 8 )', 'oracle arena 19318', '13 - 30'], ['44', 'january 23', 'cleveland', 'l 105 - 106 ( ot )', 'stephen jackson ( 24 )', 'andris biedriņš ( 13 )', 'stephen jackson ( 8 )', 'oracle arena 19596', '13 - 31'], ['45', 'january 25', 'la clippers', 'w 107 - 92 ( ot )', 'corey maggette ( 20 )', 'andris biedriņš ( 14 )', 'kelenna azubuike , stephen jackson ( 6 )', 'oracle arena 17746', '14 - 31'], ['46', 'january 28', 'dallas', 'l 93 - 117 ( ot )', 'stephen jackson ( 25 )', 'andris biedriņš ( 11 )', 'c j watson , stephen jackson , monta ellis ( 3 )', 'american airlines center 19864', '14 - 32'], ['47', 'january 30', 'new orleans', 'w 91 - 87 ( ot )', 'corey maggette ( 19 )', 'ronny turiaf ( 11 )', 'stephen jackson ( 7 )', 'new orleans arena 17738', '15 - 32'], ['48', 'january 31', 'houston', 'l 93 - 110 ( ot )', 'corey maggette ( 17 )', 'ronny turiaf ( 10 )', 'stephen jackson ( 5 )', 'toyota center 16702', '15 - 33']]

sidecarcross world championship

https://en.wikipedia.org/wiki/Sidecarcross_World_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729457-16.html.csv

comparative

daniãl willemsen/sven verbrugge had more points than janis daiders/lauris daiders .

{'row_1': '1', 'row_2': '2', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver / passenger', 'daniãl willemsen / sven verbrugge 1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose driver / passenger record fuzzily matches to daniãl willemsen / sven verbrugge 1 .', 'tostr': 'filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 } ; points }', 'tointer': 'select the rows whose driver / passenger record fuzzily matches to daniãl willemsen / sven verbrugge 1 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'driver / passenger', 'janis daiders / lauris daiders'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose driver / passenger record fuzzily matches to janis daiders / lauris daiders .', 'tostr': 'filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders } ; points }', 'tointer': 'select the rows whose driver / passenger record fuzzily matches to janis daiders / lauris daiders . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders } ; points } } = true', 'tointer': 'select the rows whose driver / passenger record fuzzily matches to daniãl willemsen / sven verbrugge 1 . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to janis daiders / lauris daiders . take the points record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; driver / passenger ; daniãl willemsen / sven verbrugge 1 } ; points } ; hop { filter_eq { all_rows ; driver / passenger ; janis daiders / lauris daiders } ; points } } = true

select the rows whose driver / passenger record fuzzily matches to daniãl willemsen / sven verbrugge 1 . take the points record of this row . select the rows whose driver / passenger record fuzzily matches to janis daiders / lauris daiders . take the points record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'driver / passenger_7': 7, 'daniãl willemsen / sven verbrugge 1_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'driver / passenger_11': 11, 'janis daiders / lauris daiders_12': 12, 'points_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'driver / passenger_7': 'driver / passenger', 'daniãl willemsen / sven verbrugge 1_8': 'daniãl willemsen / sven verbrugge 1', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'driver / passenger_11': 'driver / passenger', 'janis daiders / lauris daiders_12': 'janis daiders / lauris daiders', 'points_13': 'points'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'driver / passenger_7': [0], 'daniãl willemsen / sven verbrugge 1_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'driver / passenger_11': [1], 'janis daiders / lauris daiders_12': [1], 'points_13': [3]}

['position', 'driver / passenger', 'equipment', 'bike no', 'points']

[['1', 'daniãl willemsen / sven verbrugge 1', 'zabel - wsp', '1', '487'], ['2', 'janis daiders / lauris daiders', 'zabel - vmc', '8', '478'], ['3', 'jan hendrickx / tim smeuninx', 'zabel - vmc', '3', '405'], ['4', 'maris rupeiks / kaspars stupelis 2', 'zabel - wsp', '5', '349'], ['5', 'etienne bax / ben van den bogaart', 'zabel - vmc', '4', '347'], ['6', 'ben adriaenssen / guennady auvray', 'ktm - vmc', '6', '346'], ['7', 'ewgeny scherbinin / haralds kurpnieks', 'zabel - wsp', '20', '321'], ['8', 'marko happich / meinrad schelbert', 'zabel - vmc', '15', '317'], ['9', 'joris hendrickx / kaspars liepins', 'ktm - vmc', '2', '315'], ['10', 'daniel millard / joe millard', 'husaberg - wht', '14', '268']]

yugoslavia national football team results

https://en.wikipedia.org/wiki/Yugoslavia_national_football_team_results

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14305653-40.html.csv

count

the yugoslavia national football team played 5 friendly matches .

{'scope': 'all', 'criterion': 'equal', 'value': 'friendly', 'result': '5', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'type of game', 'friendly'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose type of game record fuzzily matches to friendly .', 'tostr': 'filter_eq { all_rows ; type of game ; friendly }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; type of game ; friendly } }', 'tointer': 'select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; type of game ; friendly } } ; 5 } = true', 'tointer': 'select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; type of game ; friendly } } ; 5 } = true

select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'type of game_5': 5, 'friendly_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'type of game_5': 'type of game', 'friendly_6': 'friendly', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'type of game_5': [0], 'friendly_6': [0], '5_7': [2]}

['date', 'city', 'opponent', 'results', 'type of game']

[['may 16', 'belgrade', 'east germany', '3:1', 'friendly'], ['may 31', 'arica , chile', 'ussr', '0:2', 'wc round 1'], ['june 2', 'arica , chile', 'uruguay', '3:1', 'wc round 1'], ['june 7', 'arica , chile', 'colombia', '5:0', 'wc round 1'], ['june 10', 'santiago , chile', 'west germany', '1:0', 'wc round 2'], ['june 13', 'vinja del mar , chile', 'czechoslovakia', '1:3', 'wc round 2'], ['june 16', 'santiago , chile', 'chile', '0:1', 'wc round 2'], ['september 16', 'leipzig , germany', 'east germany', '2:2', 'friendly'], ['september 19', 'belgrade', 'ethiopia', '5:2', 'friendly'], ['september 30', 'zagreb', 'west germany', '2:3', 'friendly'], ['october 14', 'budapest , hungary', 'hungary', '1:0', 'friendly'], ['november 4', 'belgrade', 'belgium', '3:2', "euro '64 qualifying"]]

1979 buffalo bills season

https://en.wikipedia.org/wiki/1979_Buffalo_Bills_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17386076-3.html.csv

unique

the september 9th game against cincinnati was the only one in which the bills scored over 50 points .

{'scope': 'all', 'row': '2', 'col': '4', 'col_other': '2,3', 'criterion': 'greater_than', 'value': '50', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'result', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is greater than 50 .', 'tostr': 'filter_greater { all_rows ; result ; 50 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; result ; 50 } }', 'tointer': 'select the rows whose result record is greater than 50 . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'result', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is greater than 50 .', 'tostr': 'filter_greater { all_rows ; result ; 50 }'}, 'date'], 'result': 'september 9 , 1979', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; result ; 50 } ; date }'}, 'september 9 , 1979'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; result ; 50 } ; date } ; september 9 , 1979 }', 'tointer': 'the date record of this unqiue row is september 9 , 1979 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'result', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record is greater than 50 .', 'tostr': 'filter_greater { all_rows ; result ; 50 }'}, 'opponent'], 'result': 'cincinnati bengals', 'ind': 4, 'tostr': 'hop { filter_greater { all_rows ; result ; 50 } ; opponent }'}, 'cincinnati bengals'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_greater { all_rows ; result ; 50 } ; opponent } ; cincinnati bengals }', 'tointer': 'the opponent record of this unqiue row is cincinnati bengals .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_greater { all_rows ; result ; 50 } ; date } ; september 9 , 1979 } ; eq { hop { filter_greater { all_rows ; result ; 50 } ; opponent } ; cincinnati bengals } }', 'tointer': 'the date record of this unqiue row is september 9 , 1979 . the opponent record of this unqiue row is cincinnati bengals .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_greater { all_rows ; result ; 50 } } ; and { eq { hop { filter_greater { all_rows ; result ; 50 } ; date } ; september 9 , 1979 } ; eq { hop { filter_greater { all_rows ; result ; 50 } ; opponent } ; cincinnati bengals } } } = true', 'tointer': 'select the rows whose result record is greater than 50 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1979 . the opponent record of this unqiue row is cincinnati bengals .'}

and { only { filter_greater { all_rows ; result ; 50 } } ; and { eq { hop { filter_greater { all_rows ; result ; 50 } ; date } ; september 9 , 1979 } ; eq { hop { filter_greater { all_rows ; result ; 50 } ; opponent } ; cincinnati bengals } } } = true

select the rows whose result record is greater than 50 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1979 . the opponent record of this unqiue row is cincinnati bengals .

10

8

{'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_9': 9, 'result_10': 10, '50_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'date_12': 12, 'september 9 , 1979_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'opponent_14': 14, 'cincinnati bengals_15': 15}

{'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_9': 'all_rows', 'result_10': 'result', '50_11': '50', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_12': 'date', 'september 9 , 1979_13': 'september 9 , 1979', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'opponent_14': 'opponent', 'cincinnati bengals_15': 'cincinnati bengals'}

{'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_greater_0': [1, 2, 4], 'all_rows_9': [0], 'result_10': [0], '50_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'date_12': [2], 'september 9 , 1979_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'opponent_14': [4], 'cincinnati bengals_15': [5]}

['week', 'date', 'opponent', 'result', 'attendance']

[['1', 'september 2 , 1979', 'miami dolphins', 'l 9 - 7', '69441'], ['2', 'september 9 , 1979', 'cincinnati bengals', 'w 51 - 24', '43504'], ['3', 'september 16 , 1979', 'san diego chargers', 'l 27 - 19', '50709'], ['4', 'september 23 , 1979', 'new york jets', 'w 46 - 31', '68731'], ['5', 'september 30 , 1979', 'baltimore colts', 'w 31 - 13', '31904'], ['6', 'october 7 , 1979', 'chicago bears', 'l 7 - 0', '73383'], ['7', 'october 14 , 1979', 'miami dolphins', 'l 17 - 7', '45597'], ['8', 'october 21 , 1979', 'baltimore colts', 'l 14 - 13', '50581'], ['9', 'october 28 , 1979', 'detroit lions', 'w 20 - 17', '61911'], ['10', 'november 4 , 1979', 'new england patriots', 'l 26 - 6', '67935'], ['11', 'november 11 , 1979', 'new york jets', 'w 14 - 12', '50647'], ['12', 'november 18 , 1979', 'green bay packers', 'w 19 - 12', '39679'], ['13', 'november 25 , 1979', 'new england patriots', 'w 16 - 13', '60991'], ['14', 'december 2 , 1979', 'denver broncos', 'l 19 - 16', '37886'], ['15', 'december 9 , 1979', 'minnesota vikings', 'l 10 - 3', '42239'], ['16', 'december 16 , 1979', 'pittsburgh steelers', 'l 28 - 0', '48002']]

2006 kansas city brigade season

https://en.wikipedia.org/wiki/2006_Kansas_City_Brigade_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11974088-1.html.csv

majority

the majority of games in the 2006 kansas city brigade season ended in losses for the kansas city brigade .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; result ; l } = true'}

most_eq { all_rows ; result ; l } = true

for the result records of all rows , most of them fuzzily match to l .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'l_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'l_4': 'l'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'l_4': [0]}

['week', 'date', 'opponent', 'home / away game', 'result']

[['1', 'january 29', 'dallas desperados', 'away', 'l 58 - 44'], ['2', 'february 3', 'orlando predators', 'away', 'l 48 - 41'], ['3', 'february 12', 'austin wranglers', 'home', 'l 37 - 33'], ['4', 'february 19', 'columbus destroyers', 'home', 'w 45 - 24'], ['5', 'february 24', 'georgia force', 'away', 'l 51 - 19'], ['6', 'march 5', 'tampa bay storm', 'home', 'l 69 - 59'], ['7', 'march 13', 'philadelphia soul', 'home', 'l 54 - 24'], ['8', 'march 18', 'austin wranglers', 'away', 'l 64 - 37'], ['9', 'march 24', 'new york dragons', 'away', 'l 54 - 48'], ['10', 'april 1', 'georgia force', 'home', 'l 55 - 47'], ['11', 'april 9', 'los angeles avengers', 'home', 'w 62 - 45'], ['12', 'april 16', 'colorado crush', 'home', 'l 55 - 49'], ['13', 'april 22', 'nashville kats', 'away', 'w 58 - 52'], ['14', 'april 29', 'tampa bay storm', 'away', 'l 58 - 42'], ['15', 'may 6', 'orlando predators', 'home', 'l 63 - 42'], ['16', 'may 12', 'utah blaze', 'away', 'l 55 - 54']]

list of highest mountain peaks in washington

https://en.wikipedia.org/wiki/List_of_highest_mountain_peaks_in_Washington

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19716903-1.html.csv

ordinal

mount fernow has the 15th highest prominence of all the highest mountain peaks in washington .

{'row': '7', 'col': '5', 'order': '15', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'prominence', '15'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; prominence ; 15 }'}, 'mountain peak'], 'result': 'mount fernow', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; prominence ; 15 } ; mountain peak }'}, 'mount fernow'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; prominence ; 15 } ; mountain peak } ; mount fernow } = true', 'tointer': 'select the row whose prominence record of all rows is 15th maximum . the mountain peak record of this row is mount fernow .'}

eq { hop { nth_argmax { all_rows ; prominence ; 15 } ; mountain peak } ; mount fernow } = true

select the row whose prominence record of all rows is 15th maximum . the mountain peak record of this row is mount fernow .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'prominence_5': 5, '15_6': 6, 'mountain peak_7': 7, 'mount fernow_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'prominence_5': 'prominence', '15_6': '15', 'mountain peak_7': 'mountain peak', 'mount fernow_8': 'mount fernow'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'prominence_5': [0], '15_6': [0], 'mountain peak_7': [1], 'mount fernow_8': [2]}

['rank', 'mountain peak', 'mountain range', 'elevation', 'prominence', 'isolation']

[['1', 'mount rainier', 'cascade range', '4393.293 = 14411feet 4392 m', '4027.439 = 13211feet 4027 m', '01175.46 = 730.4 miles 1175.5 km'], ['2', 'mount adams', 'cascade range', '3742.988 = 12277feet 3743 m', '2474.390 = 8116feet 2474 m', '00075.14 = 46.7 miles 75.1 km'], ['3', 'mount baker', 'cascade range', '3285.976 = 10778feet 3286 m', '2706.707 = 8878feet 2706 m', '00213.71 = 132.8 miles 213.7 km'], ['4', 'glacier peak', 'cascade range', '3213.720 = 10541feet 3286 m', '2292.378 = 7519feet 2292 m', '00090.18 = 56.0 miles 90.2 km'], ['5', 'bonanza peak', 'cascade range', '2899.695 = 9511feet 2899 m', '1131.402 = 3711feet 1131 m', '00023.04 = 14.4 miles 23.2 km'], ['6', 'mount stuart', 'cascade range', '2870.427 = 9415feet 2870 m', '1625.524 = 5335feet 1626 m', '00072.00 = 45.0 miles 72.0 km'], ['7', 'mount fernow', 'cascade range', '2819.817 = 9249feet 2819 m', '0857.012 = 2811feet 857 m', '00009.44 = 5.9 miles 9.5 km'], ['8', 'goode mountain', 'cascade range', '2804.878 = 9200feet 2810 m', '0857.012 = 3808feet 1161 m', '00027.20 = 17.0 miles 27.2 km'], ['9', 'mount shuksan', 'cascade range', '2782.622 = 9127feet 2782 m', '1340.549 = 4397feet 1340 m', '00016.64 = 10.4 miles 16.7 km'], ['10', 'buckner mountain', 'cascade range', '2778.659 = 9114feet 2778 m', '0925.000 = 3034feet 925 m', '00006.61 = 4.1 miles 6.6 km'], ['11', 'jack mountain', 'cascade range', '2764.024 = 9066feet 2763 m', '1276.220 = 4186feet 1276 m', '00026.00 = 16.3 miles 26.0 km'], ['12', 'mount spickard', 'cascade range', '2737.500 = 8979feet 2738 m', '1457.021 = 4779feet 1457 m', '00036.46 = 19.0 miles 30.5 km'], ['13', 'black peak', 'cascade range', '2734.756 = 8970feet 2734 m', '1051.829 = 3450feet 1051 m', '00008.16 = 5.1 miles 8.2 km'], ['14', 'mount redoubt', 'cascade range', '2730.488 = 8956feet 2730 m', '0502.744 = 1649feet 503 m', '00004.56 = 2.9 miles 4.6 km'], ['15', 'north gardner mountain', 'cascade range', '2730.488 = 8956feet 2730 m', '1218.293 = 3996feet 1218 m', '00043.68 = 27.3 miles 43.7 km'], ['16', 'dome peak', 'cascade range', '2719.512 = 8920feet 2719 m', '0926.829 = 3040feet 927 m', '00043.68 = 27.3 miles 43.7 km'], ['17', 'silver star mountain', 'cascade range', '2705.793 = 8875feet 2705 m', '0740.854 = 2430feet 742 m', '00007.10 = 4.4 miles 7.1 km']]

1928 army cadets football team

https://en.wikipedia.org/wiki/1928_Army_Cadets_football_team

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21091157-1.html.csv

superlative

the army cadets football team scored the most points in the game on october 13 .

{'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'black knights points'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; black knights points }'}, 'date'], 'result': 'oct 13', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; black knights points } ; date }'}, 'oct 13'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; black knights points } ; date } ; oct 13 } = true', 'tointer': 'select the row whose black knights points record of all rows is maximum . the date record of this row is oct 13 .'}

eq { hop { argmax { all_rows ; black knights points } ; date } ; oct 13 } = true

select the row whose black knights points record of all rows is maximum . the date record of this row is oct 13 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'black knights points_5': 5, 'date_6': 6, 'oct 13_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'black knights points_5': 'black knights points', 'date_6': 'date', 'oct 13_7': 'oct 13'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'black knights points_5': [0], 'date_6': [1], 'oct 13_7': [2]}

['game', 'date', 'opponent', 'result', 'black knights points', 'opponents', 'record']

[['1', 'sept 29', 'boston university', 'win', '35', '0', '1 - 0'], ['2', 'oct 6', 'southern methodist', 'win', '14', '13', '2 - 0'], ['3', 'oct 13', 'providence college', 'win', '44', '0', '3 - 0'], ['4', 'oct 20', 'harvard', 'win', '15', '0', '4 - 0'], ['5', 'oct 27', 'yale', 'win', '18', '6', '5 - 0'], ['6', 'nov 3', 'depauw', 'win', '38', '12', '6 - 0'], ['7', 'nov 10', 'notre dame', 'loss', '6', '12', '6 - 1'], ['8', 'nov 17', 'carleton', 'win', '32', '7', '7 - 1'], ['9', 'nov 24', 'nebraska', 'win', '13', '3', '8 - 1']]

dominik meffert

https://en.wikipedia.org/wiki/Dominik_Meffert

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13141391-4.html.csv

unique

the kyoto tournament was the only one in which dominik meffert used a carpet ( i ) surface .

{'scope': 'all', 'row': '7', 'col': '2', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'carpet', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet .', 'tostr': 'filter_eq { all_rows ; surface ; carpet }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; carpet } }', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet .', 'tostr': 'filter_eq { all_rows ; surface ; carpet }'}, 'tournament'], 'result': 'kyoto', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; carpet } ; tournament }'}, 'kyoto'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; kyoto }', 'tointer': 'the tournament record of this unqiue row is kyoto .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; kyoto } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is kyoto .'}

and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; kyoto } } = true

select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is kyoto .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'surface_7': 7, 'carpet_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'kyoto_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'surface_7': 'surface', 'carpet_8': 'carpet', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'kyoto_10': 'kyoto'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'surface_7': [0], 'carpet_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'kyoto_10': [3]}

['tournament', 'surface', 'partner', 'opponents in the final', 'score in the final']

[['freudenstadt', 'clay', 'tomas behrend', 'alexandre sidorenko mischa zverev', '7 - 5 , 7 - 6 5'], ['durban', 'hard', 'rik de voest', 'stéphane bohli noam okun', '6 - 4 , 6 - 2'], ['tanger', 'clay', 'steve darcis', 'uladzimir ignatik martin kližan', '5 - 7 , 7 - 5 ,'], ['pereira', 'clay', 'philipp oswald', 'gero kretschmer alex satschko', '6 - 7 4 , 7 - 6 6 ,'], ['curitiba', 'clay', 'leonardo tavares', 'ramón delgado andré sá', '3 - 6 , 6 - 2 ,'], ['nouméa', 'hard', 'frederik nielsen', 'flavio cipolla simone vagnozzi', '7 - 6 4 , 5 - 7 ,'], ['kyoto', 'carpet ( i )', 'simon stadler', 'andre begemann james lemke', '7 - 5 , 2 - 6 ,'], ['dortmund', 'clay', 'björn phau', 'teymuraz gabashvili andrey kuznetsov', '6 - 4 , 6 - 3'], ['tunis', 'clay', 'philipp oswald', 'jamie delgado andreas siljeström', '3 - 6 , 7 - 6 ( 7 - 0 ) ,']]

list of medal of honor recipients

https://en.wikipedia.org/wiki/List_of_Medal_of_Honor_recipients

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1202355-1.html.csv

count

for medal of honor recipients , when the service is the marine corps , there were 5 recipients that were privates .

{'scope': 'subset', 'criterion': 'equal', 'value': 'private', 'result': '5', 'col': '3', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'marine corps'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'service', 'marine corps'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; service ; marine corps }', 'tointer': 'select the rows whose service record fuzzily matches to marine corps .'}, 'rank', 'private'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private .', 'tostr': 'filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private }'}], 'result': '5', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } }', 'tointer': 'select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } } ; 5 } = true', 'tointer': 'select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .'}

eq { count { filter_eq { filter_eq { all_rows ; service ; marine corps } ; rank ; private } } ; 5 } = true

select the rows whose service record fuzzily matches to marine corps . among these rows , select the rows whose rank record fuzzily matches to private . the number of such rows is 5 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'service_6': 6, 'marine corps_7': 7, 'rank_8': 8, 'private_9': 9, '5_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'service_6': 'service', 'marine corps_7': 'marine corps', 'rank_8': 'rank', 'private_9': 'private', '5_10': '5'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'service_6': [0], 'marine corps_7': [0], 'rank_8': [1], 'private_9': [1], '5_10': [3]}

['name', 'service', 'rank', 'place of action', 'unit']

[['john andrews', 'navy', 'ordinary seaman', 'aboard the ussbenicia', 'ussbenicia'], ['charles brown', 'marine corps', 'corporal', 'aboard the usscolorado', 'usscolorado'], ['john coleman', 'marine corps', 'private', 'aboard the usscolorado', 'usscolorado'], ['james dougherty', 'marine corps', 'private', 'aboard the usscarondelet', 'usscarondelet'], ['frederick franklin', 'navy', 'quartermaster', 'aboard the usscolorado', 'usscolorado'], ['patrick h grace', 'navy', 'chief quartermaster', 'aboard the ussbenicia', 'ussbenicia'], ['cyrus hayden', 'navy', 'carpenter', 'aboard the usscolorado', 'usscolorado'], ['william f lukes', 'navy', 'landsman', 'ganghwa island', 'usscolorado'], ['alexander mckenzie', 'navy', "boatswain 's mate", 'aboard the usscolorado', 'usscolorado'], ['michael mcnamara', 'marine corps', 'private', 'aboard the ussbenicia', 'ussbenicia'], ['james f merton', 'navy', 'landsman', 'ganghwa island', 'usscolorado'], ['michael owens', 'marine corps', 'private', 'aboard the usscolorado', 'usscolorado'], ['hugh purvis', 'marine corps', 'private', 'aboard the ussalaska', 'ussalaska'], ['samuel f rogers', 'navy', 'quartermaster', 'aboard the usscolorado', 'usscolorado'], ['william troy', 'navy', 'ordinary seaman', 'aboard the usscolorado', 'usscolorado']]

vitamin k deficiency

https://en.wikipedia.org/wiki/Vitamin_K_deficiency

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20592988-1.html.csv

majority

the majority of conditions leave prothrombin time unaffected by the condition .

{'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'unaffected', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'prothrombin time', 'unaffected'], 'result': True, 'ind': 0, 'tointer': 'for the prothrombin time records of all rows , most of them fuzzily match to unaffected .', 'tostr': 'most_eq { all_rows ; prothrombin time ; unaffected } = true'}

most_eq { all_rows ; prothrombin time ; unaffected } = true

for the prothrombin time records of all rows , most of them fuzzily match to unaffected .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'prothrombin time_3': 3, 'unaffected_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'prothrombin time_3': 'prothrombin time', 'unaffected_4': 'unaffected'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'prothrombin time_3': [0], 'unaffected_4': [0]}

['condition', 'prothrombin time', 'partial thromboplastin time', 'bleeding time', 'platelet count']

[['vitamin k deficiency or warfarin', 'prolonged', 'normal or mildly prolonged', 'unaffected', 'unaffected'], ['disseminated intravascular coagulation', 'prolonged', 'prolonged', 'prolonged', 'decreased'], ['von willebrand disease', 'unaffected', 'prolonged or unaffected', 'prolonged', 'unaffected'], ['hemophilia', 'unaffected', 'prolonged', 'unaffected', 'unaffected'], ['aspirin', 'unaffected', 'unaffected', 'prolonged', 'unaffected'], ['thrombocytopenia', 'unaffected', 'unaffected', 'prolonged', 'decreased'], ['liver failure , early', 'prolonged', 'unaffected', 'unaffected', 'unaffected'], ['liver failure , end - stage', 'prolonged', 'prolonged', 'prolonged', 'decreased'], ['uremia', 'unaffected', 'unaffected', 'prolonged', 'unaffected'], ['congenital afibrinogenemia', 'prolonged', 'prolonged', 'prolonged', 'unaffected'], ['factor v deficiency', 'prolonged', 'prolonged', 'unaffected', 'unaffected'], ['factor x deficiency as seen in amyloid purpura', 'prolonged', 'prolonged', 'unaffected', 'unaffected'], ["glanzmann 's thrombasthenia", 'unaffected', 'unaffected', 'prolonged', 'unaffected'], ['bernard - soulier syndrome', 'unaffected', 'unaffected', 'prolonged', 'decreased or unaffected'], ['factor xii deficiency', 'unaffected', 'prolonged', 'unaffected', 'unaffected']]

partnership ( cricket )

https://en.wikipedia.org/wiki/Partnership_%28cricket%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1670921-1.html.csv

comparative

donald bradman and sid barnes scored more runs than pat symcox and mark boucher .

{'row_1': '5', 'row_2': '9', 'col': '2', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'battling partners', 'donald bradman and sid barnes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose battling partners record fuzzily matches to donald bradman and sid barnes .', 'tostr': 'filter_eq { all_rows ; battling partners ; donald bradman and sid barnes }'}, 'runs'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; battling partners ; donald bradman and sid barnes } ; runs }', 'tointer': 'select the rows whose battling partners record fuzzily matches to donald bradman and sid barnes . take the runs record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'battling partners', 'pat symcox and mark boucher'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose battling partners record fuzzily matches to pat symcox and mark boucher .', 'tostr': 'filter_eq { all_rows ; battling partners ; pat symcox and mark boucher }'}, 'runs'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; battling partners ; pat symcox and mark boucher } ; runs }', 'tointer': 'select the rows whose battling partners record fuzzily matches to pat symcox and mark boucher . take the runs record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; battling partners ; donald bradman and sid barnes } ; runs } ; hop { filter_eq { all_rows ; battling partners ; pat symcox and mark boucher } ; runs } } = true', 'tointer': 'select the rows whose battling partners record fuzzily matches to donald bradman and sid barnes . take the runs record of this row . select the rows whose battling partners record fuzzily matches to pat symcox and mark boucher . take the runs record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; battling partners ; donald bradman and sid barnes } ; runs } ; hop { filter_eq { all_rows ; battling partners ; pat symcox and mark boucher } ; runs } } = true

select the rows whose battling partners record fuzzily matches to donald bradman and sid barnes . take the runs record of this row . select the rows whose battling partners record fuzzily matches to pat symcox and mark boucher . take the runs record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'battling partners_7': 7, 'donald bradman and sid barnes_8': 8, 'runs_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'battling partners_11': 11, 'pat symcox and mark boucher_12': 12, 'runs_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'battling partners_7': 'battling partners', 'donald bradman and sid barnes_8': 'donald bradman and sid barnes', 'runs_9': 'runs', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'battling partners_11': 'battling partners', 'pat symcox and mark boucher_12': 'pat symcox and mark boucher', 'runs_13': 'runs'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'battling partners_7': [0], 'donald bradman and sid barnes_8': [0], 'runs_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'battling partners_11': [1], 'pat symcox and mark boucher_12': [1], 'runs_13': [3]}

['wicket', 'runs', 'battling partners', 'battling team', 'fielding team', 'venue', 'season']

[['1st', '415', 'gc smith and neil mckenzie', 'south africa', 'bangladesh', 'chittagong', '2008'], ['2nd', '576', 'roshan mahanama and sanath jayasuriya', 'sri lanka', 'india', 'colombo', '1997'], ['3rd', '624', 'mahela jayawardene and kumar sangakkara', 'sri lanka', 'south africa', 'colombo', '2006'], ['4th', '437', 'mahela jayawardene and thilan samaraweera', 'sri lanka', 'pakistan', 'karachi', '2008 / 09'], ['5th', '405', 'donald bradman and sid barnes', 'australia', 'england', 'sydney', '1946 / 47'], ['6th', '351', 'mahela jayawardene and prasanna jayawardene', 'sri lanka', 'india', 'ahmedabad', '2009 / 10'], ['7th', '347', 'clairmonte depeiaza and denis atkinson', 'west indies', 'australia', 'bridgetown', '1954 / 55'], ['8th', '332', 'jonathan trott and stuart broad', 'england', 'pakistan', "lord 's", '2010'], ['9th', '195', 'pat symcox and mark boucher', 'south africa', 'pakistan', 'johannesburg', '1997 / 98']]

list of intel atom microprocessors

https://en.wikipedia.org/wiki/List_of_Intel_Atom_microprocessors

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729930-11.html.csv

majority

most of the intel atom microprocessors have a frequency of less than 2 ghz .

{'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '2 ghz', 'subset': None}

{'func': 'most_less', 'args': ['all_rows', 'frequency', '2 ghz'], 'result': True, 'ind': 0, 'tointer': 'for the frequency records of all rows , most of them are less than 2 ghz .', 'tostr': 'most_less { all_rows ; frequency ; 2 ghz } = true'}

most_less { all_rows ; frequency ; 2 ghz } = true

for the frequency records of all rows , most of them are less than 2 ghz .

1

1

{'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'frequency_3': 3, '2 ghz_4': 4}

{'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'frequency_3': 'frequency', '2 ghz_4': '2 ghz'}

{'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'frequency_3': [0], '2 ghz_4': [0]}

['model number', 'sspec number', 'frequency', 'l2 cache', 'mult', 'voltage', 'socket', 'release date', 'part number ( s )', 'release price ( usd )']

[['atom z500', 'slb6q ( c0 )', '800 mhz', '512 kb', '8', '0.712 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566uc800de', '45'], ['atom z510', 'slb2c ( c0 )', '1.1 ghz', '512 kb', '11', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566uc005de', '45'], ['atom z510p', 'slgpq ( c0 )', '1.1 ghz', '512 kb', '11', '0.8 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ec005dw', 'n / a'], ['atom z510pt', 'slgpr ( c0 )', '1.1 ghz', '512 kb', '11', '0.75 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ec005dt', 'n / a'], ['atom z515', 'slgmg ( c0 )', '1.2 ghz', '512 kb', '12', '0.712 - 1v', 'bga 441', 'april 8 , 2009', 'ac80566uc009dv', 'n / a'], ['atom z520', 'slb2h ( c0 )', '1.33 ghz', '512 kb', '10', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566ue014dw', '65'], ['atom z520pt', 'slgpp ( c0 )', '1.33 ghz', '512 kb', '10', '0.9 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ee014dt', 'n / a'], ['atom z530', 'slb6p ( c0 )', '1.6 ghz', '512 kb', '12', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566ue025dw', '95'], ['atom z530p', 'slgpn ( c0 )', '1.6 ghz', '512 kb', '12', '0.8 - 1.1 v', 'bga 437', 'march 2 , 2009', 'ch80566ee025dw', 'n / a'], ['atom z540', 'slb2 m ( c0 )', '1.87 ghz', '512 kb', '14', '0.75 - 1.1 v', 'bga 441', 'april 2 , 2008', 'ac80566ue036dw', '160'], ['atom z550', 'slgpt ( c0 )', '2 ghz', '512 kb', '15', '0.75 - 1.1 v', 'bga 441', 'april 8 , 2009', 'ac80566ue041dw', '249.47 retail'], ['atom z560', 'slh63 ( c0 )', '2.13 ghz', '512 kb', '16', '0.75 - 1.1 v', 'bga 441', 'q2 2010', 'ac80566ue046dw', '144']]

1988 - 89 segunda división

https://en.wikipedia.org/wiki/1988%E2%80%9389_Segunda_Divisi%C3%B3n

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12107896-2.html.csv

count

7 teams score 50 or more goals in the 1988 - 89 segunda división .

{'scope': 'all', 'criterion': 'greater_than_eq', 'value': '50', 'result': '7', 'col': '8', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'goals for', '50'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose goals for record is greater than or equal to 50 .', 'tostr': 'filter_greater_eq { all_rows ; goals for ; 50 }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_greater_eq { all_rows ; goals for ; 50 } }', 'tointer': 'select the rows whose goals for record is greater than or equal to 50 . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater_eq { all_rows ; goals for ; 50 } } ; 7 } = true', 'tointer': 'select the rows whose goals for record is greater than or equal to 50 . the number of such rows is 7 .'}

eq { count { filter_greater_eq { all_rows ; goals for ; 50 } } ; 7 } = true

select the rows whose goals for record is greater than or equal to 50 . the number of such rows is 7 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_eq_0': 0, 'all_rows_4': 4, 'goals for_5': 5, '50_6': 6, '7_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_4': 'all_rows', 'goals for_5': 'goals for', '50_6': '50', '7_7': '7'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_eq_0': [1], 'all_rows_4': [0], 'goals for_5': [0], '50_6': [0], '7_7': [2]}

['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference']

[['1', 'cd castellón', '38', '51 + 13', '21', '9', '8', '49', '29', '+ 20'], ['2', 'rayo vallecano', '38', '49 + 11', '19', '11', '8', '61', '36', '+ 25'], ['3', 'cd tenerife', '38', '48 + 10', '20', '8', '10', '54', '36', '+ 18'], ['4', 'rcd mallorca', '38', '48 + 10', '21', '6', '11', '51', '26', '+ 25'], ['5', 'recreativo de huelva', '38', '42 + 4', '16', '10', '12', '46', '36', '+ 10'], ['6', 'racing de santander', '38', '42 + 4', '17', '8', '13', '56', '43', '+ 13'], ['7', 'ud salamanca', '38', '42 + 4', '14', '14', '10', '35', '33', '+ 2'], ['8', 'sestao', '38', '41 + 3', '14', '13', '11', '39', '32', '+ 7'], ['9', 'ue figueres', '38', '41 + 3', '16', '9', '13', '52', '50', '+ 2'], ['10', 'deportivo de la coruña', '38', '40 + 2', '16', '8', '14', '43', '35', '+ 8'], ['11', 'ud las palmas', '38', '40 + 2', '15', '10', '13', '52', '53', '- 1'], ['12', 'xerez cd', '38', '40 + 2', '13', '14', '11', '40', '38', '+ 2'], ['13', 'ce sabadell fc', '38', '39 + 1', '15', '9', '14', '49', '43', '+ 6'], ['14', 'real burgos', '38', '36 - 2', '9', '18', '11', '27', '34', '- 7'], ['15', 'castilla cf', '38', '36 - 2', '13', '10', '15', '50', '59', '- 9'], ['16', 'sd eibar', '38', '34 - 4', '8', '18', '12', '36', '42', '- 6'], ['17', 'barcelona atlètic', '38', '28 - 10', '8', '12', '18', '42', '58', '- 16'], ['18', 'ud alzira', '38', '26 - 12', '9', '8', '21', '29', '58', '- 29'], ['19', 'ue lleida', '38', '26 - 12', '8', '10', '20', '29', '43', '- 16'], ['20', 'cfj mollerussa', '38', '11 - 27', '3', '5', '30', '19', '75', '- 56']]

sebastián gonzález

https://en.wikipedia.org/wiki/Sebasti%C3%A1n_Gonz%C3%A1lez

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1257826-1.html.csv

unique

only the july 14 , 2004 goal was scored in a non-friendly competition .

{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '2004 copa américa', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2004 copa américa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to 2004 copa américa .', 'tostr': 'filter_eq { all_rows ; competition ; 2004 copa américa }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; competition ; 2004 copa américa } }', 'tointer': 'select the rows whose competition record fuzzily matches to 2004 copa américa . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2004 copa américa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to 2004 copa américa .', 'tostr': 'filter_eq { all_rows ; competition ; 2004 copa américa }'}, 'date'], 'result': '14 july 2004', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date }'}, '14 july 2004'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date } ; 14 july 2004 }', 'tointer': 'the date record of this unqiue row is 14 july 2004 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; competition ; 2004 copa américa } } ; eq { hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date } ; 14 july 2004 } } = true', 'tointer': 'select the rows whose competition record fuzzily matches to 2004 copa américa . there is only one such row in the table . the date record of this unqiue row is 14 july 2004 .'}

and { only { filter_eq { all_rows ; competition ; 2004 copa américa } } ; eq { hop { filter_eq { all_rows ; competition ; 2004 copa américa } ; date } ; 14 july 2004 } } = true

select the rows whose competition record fuzzily matches to 2004 copa américa . there is only one such row in the table . the date record of this unqiue row is 14 july 2004 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'competition_7': 7, '2004 copa américa_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '14 july 2004_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'competition_7': 'competition', '2004 copa américa_8': '2004 copa américa', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '14 july 2004_10': '14 july 2004'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'competition_7': [0], '2004 copa américa_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '14 july 2004_10': [3]}

['goal', 'date', 'score', 'result', 'competition']

[['1', '17 january 2001', '2 - 0', '2 - 0', 'friendly'], ['2', '20 january 2001', '1 - 0', '2 - 0', 'friendly'], ['3', '20 january 2001', '2 - 0', '2 - 0', 'friendly'], ['4', '15 march 2001', '3 - 1', '3 - 1', 'friendly'], ['5', '14 july 2004', '0 - 1', '1 - 1', '2004 copa américa'], ['6', '17 november 2004', '2 - 1', '2 - 1', 'friendly']]

yen plus

https://en.wikipedia.org/wiki/Yen_Plus

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18685750-1.html.csv

majority

all of the titles serialized in yen plus have not been completed .

{'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'no', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'completed', 'no'], 'result': True, 'ind': 0, 'tointer': 'for the completed records of all rows , all of them fuzzily match to no .', 'tostr': 'all_eq { all_rows ; completed ; no } = true'}

all_eq { all_rows ; completed ; no } = true

for the completed records of all rows , all of them fuzzily match to no .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'completed_3': 3, 'no_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'completed_3': 'completed', 'no_4': 'no'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'completed_3': [0], 'no_4': [0]}

['title', 'author', 'first issue', 'last issue', 'completed']

[['bamboo blade', 'masahiro totsuka ( author ) , aguri igarashi ( artist )', 'august 2008', 'may 2009', 'no'], ['black butler', 'yana toboso', 'august 2009', 'july 2010', 'no'], ['higurashi when they cry', 'ryukishi07 ( author ) , karin suzuragi ( artist )', 'august 2008', 'january 2009', 'no'], ['hero tales', 'huang jin zhou ( author ) , hiromu arakawa ( artist )', 'february 2009', 'on hiatus', 'no'], ['k - on !', 'kakifly', 'september 2010', 'ongoing', 'no'], ['nabari no ou', 'yuhki kamatani', 'august 2008', 'unknown', 'no'], ['pandora hearts', 'jun mochizuki', 'june 2009', 'unknown', 'no'], ['soul eater', 'atsushi okubo', 'august 2008', 'unknown', 'no'], ['sumomomo momomo', 'shinobu ohtaka', 'august 2008', 'october 2009', 'no'], ['yotsuba & !', 'kiyohiko azuma', 'september 2010', 'ongoing', 'no']]

2009 cfl draft

https://en.wikipedia.org/wiki/2009_CFL_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20170644-5.html.csv

count

in the 2009 cfl draft , 3 players at the rb position were drafted .

{'scope': 'all', 'criterion': 'equal', 'value': 'rb', 'result': '3', 'col': '4', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'rb'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to rb .', 'tostr': 'filter_eq { all_rows ; position ; rb }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; rb } }', 'tointer': 'select the rows whose position record fuzzily matches to rb . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; rb } } ; 3 } = true', 'tointer': 'select the rows whose position record fuzzily matches to rb . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; position ; rb } } ; 3 } = true

select the rows whose position record fuzzily matches to rb . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'position_5': 5, 'rb_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'position_5': 'position', 'rb_6': 'rb', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'rb_6': [0], '3_7': [2]}

['pick', 'cfl team', 'player', 'position', 'college']

[['33', 'hamilton tiger - cats', 'guillarme allard - cameus', 'rb', 'laval'], ['34', 'toronto argonauts', 'gordon sawler', 'dl', 'st francis xavier'], ['35', 'winnipeg blue bombers', 'peter quinney', 'fb', 'wilfrid laurier'], ['36', 'edmonton eskimos', 'eric lee', 'rb', 'weber state'], ['37', 'bc lions', 'jonathan pierre - etienne', 'de', 'montreal'], ['38', 'hamilton tiger - cats ( via saskatchewan )', 'raymond wladichuk', 'db', 'simon fraser'], ['39', 'montreal alouettes', 'benoã ® t boulanger', 'rb', 'sherbrooke']]

festivali i këngës 46

https://en.wikipedia.org/wiki/Festivali_i_K%C3%ABng%C3%ABs_46

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11513819-1.html.csv

ordinal

juliana pasha was the artist that scored the third highest amount of points at the festivali i këngës 46 .

{'row': '13', 'col': '5', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points ; 3 }'}, 'artist'], 'result': 'juliana pasha', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points ; 3 } ; artist }'}, 'juliana pasha'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points ; 3 } ; artist } ; juliana pasha } = true', 'tointer': 'select the row whose points record of all rows is 3rd maximum . the artist record of this row is juliana pasha .'}

eq { hop { nth_argmax { all_rows ; points ; 3 } ; artist } ; juliana pasha } = true

select the row whose points record of all rows is 3rd maximum . the artist record of this row is juliana pasha .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, '3_6': 6, 'artist_7': 7, 'juliana pasha_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', '3_6': '3', 'artist_7': 'artist', 'juliana pasha_8': 'juliana pasha'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], '3_6': [0], 'artist_7': [1], 'juliana pasha_8': [2]}

['draw', 'artist', 'song', 'rank', 'points', 'a krajka', 'gj leka', 'b haxhia', 'd tukiqi', 'r magjistari', 'gj xhuvani', 'a skënderaj']

[['1', 'manjola nallbani', 'kjo botë merr frymë nga dashuria', '7', '27', '3', '4', '4', '7', '8', '1', '0'], ['2', 'produkt 28', '30 sekonda', '15', '3', '0', '0', '0', '1', '1', '0', '1'], ['3', 'eneida tarifa', 'e para letër', '10', '11', '0', '1', '0', '0', '0', '7', '3'], ['4', 'mariza ikonomi', 'mall i tretur', '9', '20', '2', '3', '0', '3', '3', '3', '6'], ['5', 'greta koçi', 'natën të kërkova', '5', '35', '5', '5', '3', '6', '4', '8', '4'], ['6', 'flaka krelani & doruntina disha', 'jeta kërkon dashuri', '2', '57', '12', '12', '12', '12', '9', '0', '0'], ['7', 'mira konçi & redon makashi', 'nën një qiell', '6', '35', '6', '6', '6', '9', '6', '2', '0'], ['8', 'kthjellu', 'dhoma', '11', '9', '0', '0', '1', '0', '0', '0', '8'], ['9', 'kozma dushi', 'tatuazh në kujtesë', '16', '1', '1', '0', '0', '0', '0', '0', '0'], ['10', 'devis xherahu', 'endacaku', '17', '0', '0', '0', '0', '0', '0', '0', '0'], ['11', 'teuta kurti', 'qyteti i dashurisë', '14', '5', '0', '0', '5', '0', '0', '0', '0'], ['12', 'samanta karavello', 'pse u harrua dashuria', '8', '23', '4', '2', '2', '5', '0', '5', '5'], ['13', 'juliana pasha', 'një qiell të ri', '3', '54', '9', '9', '9', '4', '5', '9', '9'], ['14', 'agim poshka', 'kujt i them të dua', '12', '8', '0', '0', '0', '0', '2', '4', '2'], ['15', 'jonida maliqi', "s ' ka fajtor në dashuri", '4', '36', '0', '7', '7', '2', '7', '6', '7'], ['16', 'olta boka', 'zemrën e lamë peng', '1', '67', '7', '8', '8', '8', '12', '12', '12'], ['17', 'rosela gjylbegu', 'po lind një yll', '13', '8', '8', '0', '0', '0', '0', '0', '0']]

grado labs

https://en.wikipedia.org/wiki/Grado_Labs

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1601027-2.html.csv

aggregation

the average impedance for the grado labs headphones is around 33 ohms .

{'scope': 'all', 'col': '4', 'type': 'average', 'result': '33', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'impedance ( ohms )'], 'result': '33', 'ind': 0, 'tostr': 'avg { all_rows ; impedance ( ohms ) }'}, '33'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; impedance ( ohms ) } ; 33 } = true', 'tointer': 'the average of the impedance ( ohms ) record of all rows is 33 .'}

round_eq { avg { all_rows ; impedance ( ohms ) } ; 33 } = true

the average of the impedance ( ohms ) record of all rows is 33 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'impedance (ohms)_4': 4, '33_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'impedance (ohms)_4': 'impedance ( ohms )', '33_5': '33'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'impedance (ohms)_4': [0], '33_5': [1]}

['headphone model', 'headphone class', 'sensitivity ( db )', 'impedance ( ohms )', 'driver - matched db', 'construction', 'earpads', 'termination', 'succeeded by']

[['sr40', 'unknown', '100', '32', 'unknown', 'plastic', 'foam', '1 / 8 ( 3.5 mm ) plug with 1 / 4 adaptor', 'igrado'], ['sr325', 'prestige', '98', '32', '0.05', 'aluminum alloy', 'bowls', '1 / 4 ( 6.5 mm ) plug', 'sr325i'], ['hp1000', 'joseph grado signature', 'unknown', '40', 'unknown', 'aluminum alloy', 'flats', '1 / 4 ( 6.5 mm ) plug', 'no successor'], ['sr100', 'prestige', 'unknown', '32', 'unknown', 'plastic', 'flats', '1 / 4 ( 6.5 mm ) plug', 'sr125'], ['sr200', 'prestige', 'unknown', '32', 'unknown', 'plastic', 'flats', '1 / 4 ( 6.5 mm ) plug', 'sr225'], ['sr300', 'prestige', 'unknown', '32', 'unknown', 'plastic', 'flats', '1 / 4 ( 6.5 mm ) plug', 'sr325']]

2007 fedex cup playoffs

https://en.wikipedia.org/wiki/2007_FedEx_Cup_Playoffs

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13282157-1.html.csv

aggregation

the average number of events these athletes competed in was approximately 19.8 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '19.8', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'events'], 'result': '19.8', 'ind': 0, 'tostr': 'avg { all_rows ; events }'}, '19.8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; events } ; 19.8 } = true', 'tointer': 'the average of the events record of all rows is 19.8 .'}

round_eq { avg { all_rows ; events } ; 19.8 } = true

the average of the events record of all rows is 19.8 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'events_4': 4, '19.8_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'events_4': 'events', '19.8_5': '19.8'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'events_4': [0], '19.8_5': [1]}

['', 'player', 'country', 'points', 'events', 'reset points']

[['1', 'tiger woods', 'united states', '30574', '13', '100000'], ['2', 'vijay singh', 'fiji', '19129', '23', '99000'], ['3', 'jim furyk', 'united states', '16691', '19', '98500'], ['4', 'phil mickelson', 'united states', '16037', '18', '98000'], ['5', 'kj choi', 'south korea', '15485', '21', '97500'], ['6', 'rory sabbatini', 'south africa', '13548', '19', '97250'], ['7', 'zach johnson', 'united states', '13341', '19', '97000'], ['8', 'charles howell iii', 'united states', '12126', '21', '96750'], ['9', 'brandt snedeker', 'united states', '11870', '25', '96500']]

great railway journeys

https://en.wikipedia.org/wiki/Great_Railway_Journeys

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15211468-5.html.csv

superlative

the earliest episode of great railway journeys to air was entitled the gold rush line .

{'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'uk broadcast date'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; uk broadcast date }'}, 'episode title'], 'result': 'the gold rush line', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; uk broadcast date } ; episode title }'}, 'the gold rush line'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; uk broadcast date } ; episode title } ; the gold rush line } = true', 'tointer': 'select the row whose uk broadcast date record of all rows is minimum . the episode title record of this row is the gold rush line .'}

eq { hop { argmin { all_rows ; uk broadcast date } ; episode title } ; the gold rush line } = true

select the row whose uk broadcast date record of all rows is minimum . the episode title record of this row is the gold rush line .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'uk broadcast date_5': 5, 'episode title_6': 6, 'the gold rush line_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'uk broadcast date_5': 'uk broadcast date', 'episode title_6': 'episode title', 'the gold rush line_7': 'the gold rush line'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'uk broadcast date_5': [0], 'episode title_6': [1], 'the gold rush line_7': [2]}

['episode no', 'episode title', 'uk broadcast date', 'narrator', 'writer', 'details of journey', 'countries visited']

[['1', 'the gold rush line', '1983 - 02 - 15', 'simon hoggart', 'simon hoggart', 'white pass and yukon route', 'alaska , usa and yukon , canada'], ['2', 'the other poland', '1983 - 02 - 22', 'brian blessed', 'lyn webster', 'nasielsk to pułtusk & komańcza to cisna', 'poland'], ['3', 'slow train to olympia', '1983 - 03 - 01', 'michael wood', 'michael wood', 'athens to olympia', 'greece'], ['4', 'the dragons of sugar island', '1983 - 03 - 08', 'colin garratt', 'colin garratt', 'negros island', 'philippines'], ['5', 'line of dreams', '1983 - 03 - 15', 'john shrapnel', 'gerry troyna', 'jodhpur and jaipur', 'india'], ['6', 'journey to the land beyond the mountains', '1983 - 03 - 22', 'ray gosling', 'ray gosling', 'douro valley ( including the corgo line )', 'portugal'], ['7', 'the good and the quick', '1983 - 03 - 29', 'stanley reynolds', 'stanley reynolds', 'guayaquil to quito', 'ecuador']]

h. f. stephens

https://en.wikipedia.org/wiki/H._F._Stephens

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1152298-2.html.csv

ordinal

the hecate model locomotive that was designed by h. f. stephens was the third earliest to be built .

{'row': '3', 'col': '3', 'order': '3', 'col_other': '2', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'build date', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; build date ; 3 }'}, 'loco name'], 'result': 'hecate', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; build date ; 3 } ; loco name }'}, 'hecate'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; build date ; 3 } ; loco name } ; hecate } = true', 'tointer': 'select the row whose build date record of all rows is 3rd minimum . the loco name record of this row is hecate .'}

eq { hop { nth_argmin { all_rows ; build date ; 3 } ; loco name } ; hecate } = true

select the row whose build date record of all rows is 3rd minimum . the loco name record of this row is hecate .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'build date_5': 5, '3_6': 6, 'loco name_7': 7, 'hecate_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'build date_5': 'build date', '3_6': '3', 'loco name_7': 'loco name', 'hecate_8': 'hecate'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'build date_5': [0], '3_6': [0], 'loco name_7': [1], 'hecate_8': [2]}

['railway', 'loco name', 'build date', 'wheels', 'disposal']

[['kesr', 'tenterden', '1900', '2 - 4 - 0 t', 'scrapped 1941'], ['kesr', 'rolvenden', '1900', '2 - 4 - 0 t', 'scrapped 1941'], ['kesr', 'hecate', '1904', '0 - 8 - 0 t', 'to sr and br'], ['pdswjr', 'a s harris', '1907', '0 - 6 - 0 t', 'to sr and br'], ['pdswjr', 'earl of mount edgcumbe', '1907', '0 - 6 - 2 t', 'to sr and br'], ['pdswjr', 'lord st levan', '1907', '0 - 6 - 2t', 'to sr and br'], ['smr', 'pyramus', '1911', '0 - 6 - 2t', 'sold c1916'], ['smr', 'thisbe', '1911', '0 - 6 - 2t', 'sold c1916']]

list of indoor arenas in the philippines

https://en.wikipedia.org/wiki/List_of_indoor_arenas_in_the_Philippines

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12258195-2.html.csv

count

four of these arenas in the phillipines have an unknown seating capacity .

{'scope': 'all', 'criterion': 'equal', 'value': 'unknown', 'result': '4', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'maximum seating capacity', 'unknown'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose maximum seating capacity record fuzzily matches to unknown .', 'tostr': 'filter_eq { all_rows ; maximum seating capacity ; unknown }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; maximum seating capacity ; unknown } }', 'tointer': 'select the rows whose maximum seating capacity record fuzzily matches to unknown . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; maximum seating capacity ; unknown } } ; 4 } = true', 'tointer': 'select the rows whose maximum seating capacity record fuzzily matches to unknown . the number of such rows is 4 .'}

eq { count { filter_eq { all_rows ; maximum seating capacity ; unknown } } ; 4 } = true

select the rows whose maximum seating capacity record fuzzily matches to unknown . the number of such rows is 4 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'maximum seating capacity_5': 5, 'unknown_6': 6, '4_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'maximum seating capacity_5': 'maximum seating capacity', 'unknown_6': 'unknown', '4_7': '4'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'maximum seating capacity_5': [0], 'unknown_6': [0], '4_7': [2]}

['arena / venue', 'home campus', 'location', 'province / region', 'maximum seating capacity', 'year opened']

[['blue eagle gym', 'ateneo de manila university', 'quezon city', 'metro manila', '7500', '1949'], ['la salle coliseum', 'university of st la salle', 'bacolod city', 'negros occidental', '8000', '1998'], ['olivarez sports center', 'olivarez college', 'paraã ± aque city', 'metro manila', 'unknown', 'unknown'], ['quadricentennial pavilion', 'university of sto tomas', 'manila', 'metro manila', '5792', '2009'], ['san agustin gym', 'university of san agustin', 'iloilo city', 'iloilo', 'unknown', 'unknown'], ['university of baguio gym', 'university of baguio', 'baguio city', 'benguet', '5000', 'unknown'], ['west negros university gym', 'west negros university', 'bacolod city', 'negros occidental', 'unknown', 'unknown'], ['xavier university gym', 'xavier university - ateneo de cagayan', 'cagayan de oro city', 'misamis oriental', 'unknown', 'unknown'], ['holy cross of davao college gym ( hcdc )', 'holy cross of davao college', 'davao city', 'davao del sur', '7000', '2001']]

united states house of representatives elections in georgia , 1998

https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections_in_Georgia%2C_1998

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27021001-1.html.csv

comparative

john lewis was elected to his position before nathan deal was elected to his .

{'row_1': '5', 'row_2': '9', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'john lewis'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to john lewis .', 'tostr': 'filter_eq { all_rows ; incumbent ; john lewis }'}, 'elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; john lewis } ; elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to john lewis . take the elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'nathan deal'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to nathan deal .', 'tostr': 'filter_eq { all_rows ; incumbent ; nathan deal }'}, 'elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; nathan deal } ; elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to nathan deal . take the elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; john lewis } ; elected } ; hop { filter_eq { all_rows ; incumbent ; nathan deal } ; elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to john lewis . take the elected record of this row . select the rows whose incumbent record fuzzily matches to nathan deal . take the elected record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; incumbent ; john lewis } ; elected } ; hop { filter_eq { all_rows ; incumbent ; nathan deal } ; elected } } = true

select the rows whose incumbent record fuzzily matches to john lewis . take the elected record of this row . select the rows whose incumbent record fuzzily matches to nathan deal . take the elected record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'incumbent_7': 7, 'john lewis_8': 8, 'elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'nathan deal_12': 12, 'elected_13': 13}

{'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'incumbent_7': 'incumbent', 'john lewis_8': 'john lewis', 'elected_9': 'elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'nathan deal_12': 'nathan deal', 'elected_13': 'elected'}

{'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'john lewis_8': [0], 'elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'nathan deal_12': [1], 'elected_13': [3]}

['district', 'incumbent', 'party', 'elected', 'status', 'result']

[["georgia 's 1st", 'jack kingston', 'republican', '1992', 're - elected', 'jack kingston ( r ) unopposed'], ["georgia 's 2nd", 'sanford bishop', 'democratic', '1992', 're - elected', 'sanford bishop ( d ) 57 % joseph mccormick ( r ) 43 %'], ["georgia 's 3rd", 'mac collins', 'republican', '1992', 're - elected', 'mac collins ( r ) unopposed'], ["georgia 's 4th", 'cynthia mckinney', 'democratic', '1992', 're - elected', 'cynthia mckinney ( d ) 61 % sunny warren ( r ) 39 %'], ["georgia 's 5th", 'john lewis', 'democratic', '1986', 're - elected', 'john lewis ( d ) 79 % john lewis sr ( r ) 21 %'], ["georgia 's 6th", 'johnny isakson', 'republican', '1999', 're - elected', 'newt gingrich ( r ) 71 % gary pelphrey ( d ) 29 %'], ["georgia 's 7th", 'bob barr', 'republican', '1994', 're - elected', 'bob barr ( r ) 55 % james williams ( d ) 45 %'], ["georgia 's 8th", 'saxby chambliss', 'republican', '1994', 're - elected', 'saxby chambliss ( r ) 62 % ronald cain ( d ) 38 %'], ["georgia 's 9th", 'nathan deal', 'republican', '1992', 're - elected', 'nathan deal ( r ) unopposed'], ["georgia 's 10th", 'charlie norwood', 'republican', '1994', 're - elected', 'charlie norwood ( r ) 59 % marion freeman ( d ) 41 %']]

list of townships in north dakota

https://en.wikipedia.org/wiki/List_of_townships_in_North_Dakota

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18600760-10.html.csv

comparative

james hill township has more square miles of water than jim river valley township .

{'row_1': '2', 'row_2': '6', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'township', 'james hill'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose township record fuzzily matches to james hill .', 'tostr': 'filter_eq { all_rows ; township ; james hill }'}, 'water ( sqmi )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; township ; james hill } ; water ( sqmi ) }', 'tointer': 'select the rows whose township record fuzzily matches to james hill . take the water ( sqmi ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'township', 'jim river valley'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose township record fuzzily matches to jim river valley .', 'tostr': 'filter_eq { all_rows ; township ; jim river valley }'}, 'water ( sqmi )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; township ; jim river valley } ; water ( sqmi ) }', 'tointer': 'select the rows whose township record fuzzily matches to jim river valley . take the water ( sqmi ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; township ; james hill } ; water ( sqmi ) } ; hop { filter_eq { all_rows ; township ; jim river valley } ; water ( sqmi ) } } = true', 'tointer': 'select the rows whose township record fuzzily matches to james hill . take the water ( sqmi ) record of this row . select the rows whose township record fuzzily matches to jim river valley . take the water ( sqmi ) record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; township ; james hill } ; water ( sqmi ) } ; hop { filter_eq { all_rows ; township ; jim river valley } ; water ( sqmi ) } } = true

select the rows whose township record fuzzily matches to james hill . take the water ( sqmi ) record of this row . select the rows whose township record fuzzily matches to jim river valley . take the water ( sqmi ) record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'township_7': 7, 'james hill_8': 8, 'water (sqmi)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'township_11': 11, 'jim river valley_12': 12, 'water (sqmi)_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'township_7': 'township', 'james hill_8': 'james hill', 'water (sqmi)_9': 'water ( sqmi )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'township_11': 'township', 'jim river valley_12': 'jim river valley', 'water (sqmi)_13': 'water ( sqmi )'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'township_7': [0], 'james hill_8': [0], 'water (sqmi)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'township_11': [1], 'jim river valley_12': [1], 'water (sqmi)_13': [3]}

['township', 'county', 'pop ( 2010 )', 'land ( sqmi )', 'water ( sqmi )', 'latitude', 'longitude', 'geo id', 'ansi code']

[['jackson', 'sargent', '33', '35.809', '0.000', '46.066276', '- 97.945530', '3808140460', '1036797'], ['james hill', 'mountrail', '32', '31.820', '4.243', '48.423125', '- 102.429934', '3806140500', '1037048'], ['james river valley', 'dickey', '40', '28.597', '0.000', '46.246641', '- 98.188329', '3802140540', '1036767'], ['janke', 'logan', '28', '35.995', '0.163', '46.415512', '- 99.131701', '3804740620', '1037193'], ['jefferson', 'pierce', '45', '35.069', '1.125', '48.232149', '- 100.182370', '3806940700', '1759556'], ['jim river valley', 'stutsman', '38', '34.134', '1.746', '47.112388', '- 98.778478', '3809340780', '1036484'], ['johnson', 'wells', '36', '35.299', '0.908', '47.377745', '- 99.458677', '3810340820', '1037137'], ['johnstown', 'grand forks', '79', '36.199', '0.000', '48.151362', '- 97.449033', '3803540940', '1036624'], ['joliette', 'pembina', '67', '70.044', '0.771', '48.796545', '- 97.217227', '3806741020', '1036723']]

looney tunes and merrie melodies filmography ( 1929 - 39 )

https://en.wikipedia.org/wiki/Looney_Tunes_and_Merrie_Melodies_filmography_%281929%E2%80%9339%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18792938-2.html.csv

ordinal

the film titled ' red - headed baby ' had the third highest production number of the 1929-39 looney tunes and merrie melodies films .

{'row': '17', 'col': '4', 'order': '3', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'production num', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; production num ; 3 }'}, 'title'], 'result': 'red - headed baby', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; production num ; 3 } ; title }'}, 'red - headed baby'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; production num ; 3 } ; title } ; red - headed baby } = true', 'tointer': 'select the row whose production num record of all rows is 3rd maximum . the title record of this row is red - headed baby .'}

eq { hop { nth_argmax { all_rows ; production num ; 3 } ; title } ; red - headed baby } = true

select the row whose production num record of all rows is 3rd maximum . the title record of this row is red - headed baby .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'production num_5': 5, '3_6': 6, 'title_7': 7, 'red - headed baby_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'production num_5': 'production num', '3_6': '3', 'title_7': 'title', 'red - headed baby_8': 'red - headed baby'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'production num_5': [0], '3_6': [0], 'title_7': [1], 'red - headed baby_8': [2]}

['title', 'series', 'characters', 'production num', 'release date']

[['big man from the north', 'lt', 'bosko , honey', '4500', '1931 - 01 - xx'], ["ai n't nature grand !", 'lt', 'bosko', '4626', '1931 - 03 - xx'], ["ups 'n downs", 'lt', 'bosko', '4640', '1931 - 03 - xx'], ['dumb patrol', 'lt', 'bosko , honey', '4664', '1931 - 05 - xx'], ['yodeling yokels', 'lt', 'bosko , honey', '4680', '1931 - 06 - xx'], ["bosko 's holiday", 'lt', 'bosko , honey', '4694', '1931 - 07 - xx'], ["the tree 's knees", 'lt', 'bosko', '4725', '1931 - 07 - xx'], ['lady , play your mandolin !', 'mm', 'animals ( cartoon character ) , foxy , roxy', '4645', '1931 - 08 - xx'], ['smile , darn ya , smile !', 'mm', 'foxy , radio , roxy', '4825', '1931 - 09 - 05'], ['bosko shipwrecked', 'lt', 'bosko', '4666', '1931 - 09 - 19'], ['one more time', 'mm', 'foxy , mugs , roxy', '4851', '1931 - 10 - 03'], ['bosko the doughboy', 'lt', 'bosko', '5017', '1931 - 10 - 17'], ["you do n't know what you 're doin '", 'mm', 'fluffy , piggy , the car', '4977', '1931 - 10 - 31'], ["bosko 's soda fountain", 'lt', 'bosko', '5045', '1931 - 11 - 14'], ["hittin ' the trail for hallelujah land", 'mm', 'banjo player , fluffy', '5025', '11 / 28 / 31'], ["bosko 's fox hunt", 'lt', 'bosko , bruno', '5046', '1931 - 12 - 12'], ['red - headed baby', 'mm', 'red - headed baby , toymaker', '5038', '1931 - 12 - 26']]

2000 pga championship

https://en.wikipedia.org/wiki/2000_PGA_Championship

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18026889-4.html.csv

majority

most of the players at the 2000 pga championship to score 70 were from the united states .

{'scope': 'subset', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'united states', 'subset': {'col': '4', 'criterion': 'equal', 'value': '70'}}

{'func': 'most_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'score', '70'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; score ; 70 }', 'tointer': 'select the rows whose score record is equal to 70 .'}, 'country', 'united states'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose score record is equal to 70 . for the country records of these rows , most of them fuzzily match to united states .', 'tostr': 'most_eq { filter_eq { all_rows ; score ; 70 } ; country ; united states } = true'}

most_eq { filter_eq { all_rows ; score ; 70 } ; country ; united states } = true

select the rows whose score record is equal to 70 . for the country records of these rows , most of them fuzzily match to united states .

2

2

{'most_str_eq_1': 1, 'result_2': 2, 'filter_eq_0': 0, 'all_rows_3': 3, 'score_4': 4, '70_5': 5, 'country_6': 6, 'united states_7': 7}

{'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_eq_0': 'filter_eq', 'all_rows_3': 'all_rows', 'score_4': 'score', '70_5': '70', 'country_6': 'country', 'united states_7': 'united states'}

{'most_str_eq_1': [2], 'result_2': [], 'filter_eq_0': [1], 'all_rows_3': [0], 'score_4': [0], '70_5': [0], 'country_6': [1], 'united states_7': [1]}

['place', 'player', 'country', 'score', 'to par']

[['t1', 'scott dunlap', 'united states', '66', '- 6'], ['t1', 'tiger woods', 'united states', '66', '- 6'], ['t3', 'darren clarke', 'northern ireland', '68', '- 4'], ['t3', 'davis love iii', 'united states', '68', '- 4'], ['t5', 'stephen ames', 'trinidad and tobago', '69', '- 3'], ['t5', 'ed fryatt', 'england', '69', '- 3'], ['t5', 'fred funk', 'united states', '69', '- 3'], ['t5', 'j p hayes', 'united states', '69', '- 3'], ['t9', 'stuart appleby', 'australia', '70', '- 2'], ['t9', 'brian henninger', 'united states', '70', '- 2'], ['t9', 'miguel ángel jiménez', 'spain', '70', '- 2'], ['t9', 'jonathan kaye', 'united states', '70', '- 2'], ['t9', 'tom kite', 'united states', '70', '- 2'], ['t9', 'phil mickelson', 'united states', '70', '- 2'], ['t9', 'jean van de velde', 'france', '70', '- 2']]

simon shirley

https://en.wikipedia.org/wiki/Simon_Shirley

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15170292-1.html.csv

unique

the summer olympics was the only competition held in south korea .

{'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'south korea', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'south korea'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to south korea .', 'tostr': 'filter_eq { all_rows ; venue ; south korea }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; south korea } }', 'tointer': 'select the rows whose venue record fuzzily matches to south korea . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'south korea'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to south korea .', 'tostr': 'filter_eq { all_rows ; venue ; south korea }'}, 'tournament'], 'result': 'summer olympics', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; south korea } ; tournament }'}, 'summer olympics'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; south korea } ; tournament } ; summer olympics }', 'tointer': 'the tournament record of this unqiue row is summer olympics .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; venue ; south korea } } ; eq { hop { filter_eq { all_rows ; venue ; south korea } ; tournament } ; summer olympics } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to south korea . there is only one such row in the table . the tournament record of this unqiue row is summer olympics .'}

and { only { filter_eq { all_rows ; venue ; south korea } } ; eq { hop { filter_eq { all_rows ; venue ; south korea } ; tournament } ; summer olympics } } = true

select the rows whose venue record fuzzily matches to south korea . there is only one such row in the table . the tournament record of this unqiue row is summer olympics .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'venue_7': 7, 'south korea_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'summer olympics_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'venue_7': 'venue', 'south korea_8': 'south korea', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'summer olympics_10': 'summer olympics'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'venue_7': [0], 'south korea_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'summer olympics_10': [3]}

['year', 'tournament', 'venue', 'result', 'event']

[['1988', 'summer olympics', 'seoul , south korea', '15th', 'decathlon'], ['1994', 'hypo - meeting', 'götzis , austria', '11th', 'decathlon'], ['1994', 'commonwealth games', 'victoria , canada', '2nd', 'decathlon'], ['1995', 'hypo - meeting', 'götzis , austria', '20th', 'decathlon'], ['1996', 'hypo - meeting', 'götzis , austria', '19th', 'decathlon']]

south asian canadian

https://en.wikipedia.org/wiki/South_Asian_Canadian

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1717824-1.html.csv

superlative

ontario had more south asians in 2011 than any other province in canada .

{'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'south asians 2011'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; south asians 2011 }'}, 'province'], 'result': 'ontario', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; south asians 2011 } ; province }'}, 'ontario'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; south asians 2011 } ; province } ; ontario } = true', 'tointer': 'select the row whose south asians 2011 record of all rows is maximum . the province record of this row is ontario .'}

eq { hop { argmax { all_rows ; south asians 2011 } ; province } ; ontario } = true

select the row whose south asians 2011 record of all rows is maximum . the province record of this row is ontario .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'south asians 2011_5': 5, 'province_6': 6, 'ontario_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'south asians 2011_5': 'south asians 2011', 'province_6': 'province', 'ontario_7': 'ontario'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'south asians 2011_5': [0], 'province_6': [1], 'ontario_7': [2]}

['province', 'south asians 2001', '% 2001', 'south asians 2011', '% 2011']

[['ontario', '554870', '4.9 %', '1003180', '7.9 %'], ['british columbia', '210295', '5.4 %', '311265', '7.2 %'], ['alberta', '69580', '2.4 %', '159055', '4.4 %'], ['quebec', '59510', '0.8 %', '91400', '1.2 %'], ['manitoba', '12875', '1.2 %', '26220', '2.2 %'], ['saskatchewan', '4090', '0.4 %', '12620', '1.3 %'], ['nova scotia', '2895', '0.3 %', '5935', '0.7 %'], ['new brunswick', '1415', '0.2 %', '3090', '0.4 %'], ['newfoundland and labrador', '1010', '0.2 %', '2005', '0.4 %'], ['prince edward island', '115', '0.1 %', '500', '0.4 %'], ['yukon', '205', '0.7 %', '340', '1.0 %'], ['northwest territories', '190', '0.5 %', '200', '0.5 %'], ['nunavut', '30', '0.1 %', '115', '0.4 %']]

2005 - 06 liverpool f.c. season

https://en.wikipedia.org/wiki/2005%E2%80%9306_Liverpool_F.C._season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19764939-1.html.csv

count

2 players were ranked 6th in the 2005 - 06 liverpool f.c. season .

{'scope': 'all', 'criterion': 'equal', 'value': '6', 'result': '2', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'rank', '6'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose rank record is equal to 6 .', 'tostr': 'filter_eq { all_rows ; rank ; 6 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; rank ; 6 } }', 'tointer': 'select the rows whose rank record is equal to 6 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; rank ; 6 } } ; 2 } = true', 'tointer': 'select the rows whose rank record is equal to 6 . the number of such rows is 2 .'}

eq { count { filter_eq { all_rows ; rank ; 6 } } ; 2 } = true

select the rows whose rank record is equal to 6 . the number of such rows is 2 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'rank_5': 5, '6_6': 6, '2_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'rank_5': 'rank', '6_6': '6', '2_7': '2'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'rank_5': [0], '6_6': [0], '2_7': [2]}

['rank', 'no', 'pos', 'player', 'premier league', 'fa cup', 'league cup', 'champions league', 'club world cup', 'total']

[['1', '8', 'mf', 'steven gerrard', '10', '4', '1', '7', '1', '23'], ['2', '9', 'fw', 'djibril cisse', '9', '2', '0', '6', '0', '19'], ['3', '15', 'fw', 'peter crouch', '8', '3', '0', '0', '2', '13'], ['4', '10', 'mf', 'luis garcã\xada', '7', '1', '0', '2', '0', '11'], ['5', '19', 'fw', 'fernando morientes', '5', '1', '0', '3', '0', '9'], ['6', '11', 'fw', 'robbie fowler', '5', '0', '0', '0', '0', '5'], ['6', '14', 'mf', 'xabi alonso', '3', '2', '0', '0', '0', '5'], ['8', '6', 'df', 'john arne riise', '1', '3', '0', '0', '0', '4'], ['9', '7', 'mf', 'harry kewell', '3', '0', '0', '0', '0', '3'], ['9', '24', 'fw', 'florent sinama - pongolle', '0', '2', '0', '1', '0', '3'], ['11', '4', 'df', 'sami hyypia', '1', '1', '0', '0', '0', '2'], ['11', '30', 'mf', 'boudewijn zenden', '2', '0', '0', '0', '0', '2'], ['13', '23', 'df', 'jamie carragher', '0', '0', '0', '1', '0', '1'], ['13', '28', 'df', 'stephen warnock', '1', '0', '0', '0', '0', '1']]

european poker tour

https://en.wikipedia.org/wiki/European_Poker_Tour

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1296513-4.html.csv

comparative

the barcelona open had a higher prize amount than the european poker championships .

{'row_1': '3', 'row_2': '2', 'col': '5', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'ept baden classic'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose event record fuzzily matches to ept baden classic .', 'tostr': 'filter_eq { all_rows ; event ; ept baden classic }'}, 'prize'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; event ; ept baden classic } ; prize }', 'tointer': 'select the rows whose event record fuzzily matches to ept baden classic . take the prize record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'event', 'the european poker championships'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose event record fuzzily matches to the european poker championships .', 'tostr': 'filter_eq { all_rows ; event ; the european poker championships }'}, 'prize'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; event ; the european poker championships } ; prize }', 'tointer': 'select the rows whose event record fuzzily matches to the european poker championships . take the prize record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; event ; ept baden classic } ; prize } ; hop { filter_eq { all_rows ; event ; the european poker championships } ; prize } } = true', 'tointer': 'select the rows whose event record fuzzily matches to ept baden classic . take the prize record of this row . select the rows whose event record fuzzily matches to the european poker championships . take the prize record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; event ; ept baden classic } ; prize } ; hop { filter_eq { all_rows ; event ; the european poker championships } ; prize } } = true

select the rows whose event record fuzzily matches to ept baden classic . take the prize record of this row . select the rows whose event record fuzzily matches to the european poker championships . take the prize record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'event_7': 7, 'ept baden classic_8': 8, 'prize_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'event_11': 11, 'the european poker championships_12': 12, 'prize_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'event_7': 'event', 'ept baden classic_8': 'ept baden classic', 'prize_9': 'prize', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'event_11': 'event', 'the european poker championships_12': 'the european poker championships', 'prize_13': 'prize'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'event_7': [0], 'ept baden classic_8': [0], 'prize_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'event_11': [1], 'the european poker championships_12': [1], 'prize_13': [3]}

['date', 'city', 'event', 'winner', 'prize']

[['28 aug - 2 sep 2007', 'barcelona', 'barcelona open', 'sander lyloff', '1170700'], ['25 - 29 september 2007', 'london', 'the european poker championships', 'joseph mouawad', '611520'], ['7 - 10 october 2007', 'baden', 'ept baden classic', 'julian thew', '670800'], ['30 oct - 3 nov 2007', 'dublin', 'ept dublin', 'reuben peters', '532620'], ['10 - 14 december 2007', 'prague', 'ept prague', 'arnaud mattern', '708400'], ['5 - 10 january 2008', 'paradise island', 'ept pokerstars caribbean adventure', 'bertrand grospellier', '2000000'], ['29 jan - 2 feb 2008', 'dortmund', 'ept german open', 'michael mcdonald', '933600'], ['19 - 23 february 2008', 'copenhagen', 'ept scandinavian open', 'tim vance', 'kr6220488'], ['11 - 15 march 2008', 'warsaw', 'ept polish open', 'michael schulze', 'zł2153999'], ['1 - 5 april 2008', 'sanremo', 'ept sanremo', 'jason mercier', '869000'], ['12 - 17 april 2008', 'monte carlo', 'european poker tour grand final', 'glen chorny', '2020000']]

hong kong first division league

https://en.wikipedia.org/wiki/Hong_Kong_First_Division_League

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1908877-2.html.csv

superlative

south china has the most top division titles by far , with 41 .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'top division titles'], 'result': '41', 'ind': 0, 'tostr': 'max { all_rows ; top division titles }', 'tointer': 'the maximum top division titles record of all rows is 41 .'}, '41'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; top division titles } ; 41 }', 'tointer': 'the maximum top division titles record of all rows is 41 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'top division titles'], 'result': None, 'ind': 2, 'tostr': 'argmax { all_rows ; top division titles }'}, 'club'], 'result': 'south china', 'ind': 3, 'tostr': 'hop { argmax { all_rows ; top division titles } ; club }'}, 'south china'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { argmax { all_rows ; top division titles } ; club } ; south china }', 'tointer': 'the club record of the row with superlative top division titles record is south china .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { max { all_rows ; top division titles } ; 41 } ; eq { hop { argmax { all_rows ; top division titles } ; club } ; south china } } = true', 'tointer': 'the maximum top division titles record of all rows is 41 . the club record of the row with superlative top division titles record is south china .'}

and { eq { max { all_rows ; top division titles } ; 41 } ; eq { hop { argmax { all_rows ; top division titles } ; club } ; south china } } = true

the maximum top division titles record of all rows is 41 . the club record of the row with superlative top division titles record is south china .

6

6

{'and_5': 5, 'result_6': 6, 'eq_1': 1, 'max_0': 0, 'all_rows_7': 7, 'top division titles_8': 8, '41_9': 9, 'str_eq_4': 4, 'str_hop_3': 3, 'argmax_2': 2, 'all_rows_10': 10, 'top division titles_11': 11, 'club_12': 12, 'south china_13': 13}

{'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'max_0': 'max', 'all_rows_7': 'all_rows', 'top division titles_8': 'top division titles', '41_9': '41', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'argmax_2': 'argmax', 'all_rows_10': 'all_rows', 'top division titles_11': 'top division titles', 'club_12': 'club', 'south china_13': 'south china'}

{'and_5': [6], 'result_6': [], 'eq_1': [5], 'max_0': [1], 'all_rows_7': [0], 'top division titles_8': [0], '41_9': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'argmax_2': [3], 'all_rows_10': [2], 'top division titles_11': [2], 'club_12': [3], 'south china_13': [4]}

['club', 'position in 2012 - 13', 'first season in top division', 'number of seasons in top division', 'first season of current spell in top division', 'top division titles', 'last top division title']

[['biu chun rangers', '6th', '1965 - 66', '35', '2012 - 13', '1', '1970 - 71'], ['citizen', '8th', '2004 - 05', '9', '2004 - 05', '0', 'n / a'], ['eastern salon', '3rd , second division', '1936 - 37', '59', '2013 - 14', '4', '1994 - 95'], ['happy valley', '2nd , second division', '1959 - 60', '48', '2013 - 14', '6', '2005 - 06'], ['kitchee', '2nd', '1947 - 48', '35', '2003 - 04', '5', '2011 - 12'], ['south china', '1st', '1918 - 19', '93', '1918 - 19', '41', '2012 - 13'], ['southern', '4th', '2011 - 12', '2', '2012 - 13', '0', 'n / a'], ['sun pegasus', '5th', '2008 - 09', '6', '2008 - 09', '0', 'n / a'], ['sunray cave jc sun hei', '7th', '1994 - 95', '20', '1994 - 95', '3', '2004 - 05'], ['tuen mun', '3rd', '2010 - 11', '4', '2010 - 11', '0', 'n / a'], ['yokohama fc hong kong', '9th', '2012 - 13', '2', '2012 - 13', '0', 'n / a']]

sriperumbudur ( lok sabha constituency )

https://en.wikipedia.org/wiki/Sriperumbudur_%28Lok_Sabha_constituency%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18640543-1.html.csv

unique

in sriperumbudur , when the party affiliation is dravida munnetra kazhagam , the only time the mp was t r baalu , was from 2009-incumbent .

{'scope': 'subset', 'row': '12', 'col': '3', 'col_other': '2,4', 'criterion': 'equal', 'value': 't r baalu', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'dravida munnetra kazhagam'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party affiliation', 'dravida munnetra kazhagam'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam }', 'tointer': 'select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam .'}, 'name of mp', 't r baalu'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu .', 'tostr': 'filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } }', 'tointer': 'select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party affiliation', 'dravida munnetra kazhagam'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam }', 'tointer': 'select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam .'}, 'name of mp', 't r baalu'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu .', 'tostr': 'filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu }'}, 'duration'], 'result': '2009 - incumbent', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } ; duration }'}, '2009 - incumbent'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } ; duration } ; 2009 - incumbent }', 'tointer': 'the duration record of this unqiue row is 2009 - incumbent .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } } ; eq { hop { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } ; duration } ; 2009 - incumbent } } = true', 'tointer': 'select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu . there is only one such row in the table . the duration record of this unqiue row is 2009 - incumbent .'}

and { only { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } } ; eq { hop { filter_eq { filter_eq { all_rows ; party affiliation ; dravida munnetra kazhagam } ; name of mp ; t r baalu } ; duration } ; 2009 - incumbent } } = true

select the rows whose party affiliation record fuzzily matches to dravida munnetra kazhagam . among these rows , select the rows whose name of mp record fuzzily matches to t r baalu . there is only one such row in the table . the duration record of this unqiue row is 2009 - incumbent .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'party affiliation_8': 8, 'dravida munnetra kazhagam_9': 9, 'name of mp_10': 10, 't r baalu_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'duration_12': 12, '2009 - incumbent_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'party affiliation_8': 'party affiliation', 'dravida munnetra kazhagam_9': 'dravida munnetra kazhagam', 'name of mp_10': 'name of mp', 't r baalu_11': 't r baalu', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'duration_12': 'duration', '2009 - incumbent_13': '2009 - incumbent'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'party affiliation_8': [0], 'dravida munnetra kazhagam_9': [0], 'name of mp_10': [1], 't r baalu_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'duration_12': [3], '2009 - incumbent_13': [4]}

['lok sabha', 'duration', 'name of mp', 'party affiliation', 'election year']

[['fourth', '1967 - 71', 'p sivasankaran', 'dravida munnetra kazhagam', '1967'], ['fifth', '1971 - 77', 'ts lakshmanan', 'dravida munnetra kazhagam', '1971'], ['sixth', '1977 - 80', 'seeralan jaganathan', 'all india anna dravida munnetra kazhagam', '1977'], ['seventh', '1980 - 84', 't nagaratnam', 'dravida munnetra kazhagam', '1980'], ['eighth', '1984 - 89', 'margatham chandrasekar', 'indian national congress', '1984'], ['ninth', '1989 - 91', 'margatham chandrasekar', 'indian national congress', '1989'], ['tenth', '1991 - 96', 'margatham chandrasekar', 'indian national congress', '1991'], ['elewenth', '1996 - 98', 't nagaratnam', 'dravida munnetra kazhagam', '1996'], ['twelfth', '1998 - 99', 'k venugopal', 'all india anna dravida munnetra kazhagam', '1998'], ['thirteenth', '1999 - 04', 'a krishnaswamy', 'dravida munnetra kazhagam', '1999'], ['fourteenth', '2004 - 09', 'a krishnaswamy', 'dravida munnetra kazhagam', '2004'], ['fifteenth', '2009 - incumbent', 't r baalu', 'dravida munnetra kazhagam', '2009']]

1970 cfl draft

https://en.wikipedia.org/wiki/1970_CFL_Draft

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26996293-2.html.csv

ordinal

burns mcpherson was the second-highest pick in the second round of the 1970 cfl draft .

{'row': '2', 'col': '1', 'order': '2', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'pick', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; pick ; 2 }'}, 'player'], 'result': 'burns mcpherson', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; pick ; 2 } ; player }'}, 'burns mcpherson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; burns mcpherson } = true', 'tointer': 'select the row whose pick record of all rows is 2nd minimum . the player record of this row is burns mcpherson .'}

eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; burns mcpherson } = true

select the row whose pick record of all rows is 2nd minimum . the player record of this row is burns mcpherson .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, '2_6': 6, 'player_7': 7, 'burns mcpherson_8': 8}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'pick_5': 'pick', '2_6': '2', 'player_7': 'player', 'burns mcpherson_8': 'burns mcpherson'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], '2_6': [0], 'player_7': [1], 'burns mcpherson_8': [2]}

['pick', 'cfl team', 'player', 'position', 'college']

[['10', 'winnipeg ( 2 )', 'john senst', 'fl', 'simon fraser'], ['11', 'montreal ( 1 )', 'burns mcpherson', 'hb', 'st francis xavier'], ['12', 'edmonton ( 2 )', 'jim henshall', 'hb', 'western'], ['13', 'bc lions ( 2 )', "tony d'aloisio", 'fb', 'windsor'], ['14', 'winnipeg ( 3 ) via hamilton', 'rick sugden', 'hb', 'simon fraser'], ['15', 'calgary ( 3 )', 'don lumb', 'ot', 'british columbia'], ['16', 'toronto ( 1 )', 'paul brown', 'ot', 'waterloo lutheran'], ['17', 'saskatchewan ( 2 )', 'andre rancourt', 'de', 'ottawa']]

2005 houston astros season

https://en.wikipedia.org/wiki/2005_Houston_Astros_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13710464-1.html.csv

count

chris young was the winning pitcher twice when texas won against houston in the 2005 lone star series .

{'scope': 'subset', 'criterion': 'equal', 'value': 'chris young', 'result': '2', 'col': '4', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'texas'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning team', 'texas'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; winning team ; texas }', 'tointer': 'select the rows whose winning team record fuzzily matches to texas .'}, 'winning pitcher', 'chris young'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young .', 'tostr': 'filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young } }', 'tointer': 'select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young } } ; 2 } = true', 'tointer': 'select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young . the number of such rows is 2 .'}

eq { count { filter_eq { filter_eq { all_rows ; winning team ; texas } ; winning pitcher ; chris young } } ; 2 } = true

select the rows whose winning team record fuzzily matches to texas . among these rows , select the rows whose winning pitcher record fuzzily matches to chris young . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'winning team_6': 6, 'texas_7': 7, 'winning pitcher_8': 8, 'chris young_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'winning team_6': 'winning team', 'texas_7': 'texas', 'winning pitcher_8': 'winning pitcher', 'chris young_9': 'chris young', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'winning team_6': [0], 'texas_7': [0], 'winning pitcher_8': [1], 'chris young_9': [1], '2_10': [3]}

['date', 'winning team', 'score', 'winning pitcher', 'losing pitcher', 'attendance', 'location']

[['may 20', 'texas', '7 - 3', 'kenny rogers', 'brandon backe', '38109', 'arlington'], ['may 21', 'texas', '18 - 3', 'chris young', 'ezequiel astacio', '35781', 'arlington'], ['may 22', 'texas', '2 - 0', 'chan ho park', 'roy oswalt', '40583', 'arlington'], ['june 24', 'houston', '5 - 2', 'roy oswalt', 'ricardo rodriguez', '36199', 'houston'], ['june 25', 'texas', '6 - 5', 'chris young', 'brandon backe', '41868', 'houston'], ['june 26', 'houston', '3 - 2', 'chad qualls', 'juan dominguez', '35331', 'houston']]

sexuality of adolf hitler

https://en.wikipedia.org/wiki/Sexuality_of_Adolf_Hitler

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13941408-1.html.csv

unique

eva braun was the only wife of adolf hitler .

{'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'wife', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'relationship', 'wife'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose relationship record fuzzily matches to wife .', 'tostr': 'filter_eq { all_rows ; relationship ; wife }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; relationship ; wife } }', 'tointer': 'select the rows whose relationship record fuzzily matches to wife . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'relationship', 'wife'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose relationship record fuzzily matches to wife .', 'tostr': 'filter_eq { all_rows ; relationship ; wife }'}, 'name'], 'result': 'eva braun', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; relationship ; wife } ; name }'}, 'eva braun'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; relationship ; wife } ; name } ; eva braun }', 'tointer': 'the name record of this unqiue row is eva braun .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; relationship ; wife } } ; eq { hop { filter_eq { all_rows ; relationship ; wife } ; name } ; eva braun } } = true', 'tointer': 'select the rows whose relationship record fuzzily matches to wife . there is only one such row in the table . the name record of this unqiue row is eva braun .'}

and { only { filter_eq { all_rows ; relationship ; wife } } ; eq { hop { filter_eq { all_rows ; relationship ; wife } ; name } ; eva braun } } = true

select the rows whose relationship record fuzzily matches to wife . there is only one such row in the table . the name record of this unqiue row is eva braun .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'relationship_7': 7, 'wife_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'eva braun_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'relationship_7': 'relationship', 'wife_8': 'wife', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'eva braun_10': 'eva braun'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'relationship_7': [0], 'wife_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'eva braun_10': [3]}

['name', 'life', 'age at death', 'first contact with hitler', 'relationship']

[['stefanie rabatsch', 'unknown', 'unknown', 'c 1905', 'teenage love interest'], ['charlotte lobjoie', '1898 - 1951', '53', 'allegedly met in 1917', 'poorly substantiated claim that she bore his child'], ['eva braun', 'february 6 , 1912 - april 30 , 1945', '33', 'met in autumn 1929', 'wife'], ['geli raubal', 'june 4 , 1908 - september 18 , 1931', '23', 'lived with hitler in 1925', 'niece , speculated lovers'], ['erna hanfstaengl', '1885 - 1981', '96', 'met in 1920s', 'rumoured lovers'], ['renate müller', 'april 26 , 1906 - october 7 , 1937', '31', 'met in 1930s', 'alleged single sexual encounter'], ['maria reiter', 'december 23 , 1911 - 1992', '81', 'met in 1927', 'possibly lovers'], ['unity mitford', 'august 8 , 1914 - may 28 , 1948', '33', 'met in 1934', 'friends , speculated lovers']]

indiana high school athletics conferences : allen county - metropolitan

https://en.wikipedia.org/wiki/Indiana_High_School_Athletics_Conferences%3A_Allen_County_%E2%80%93_Metropolitan

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13986492-1.html.csv

majority

all of the schools in allen county , in indiana high school athletics conferences , have an enrollment that is under 1000 .

{'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'less_than', 'value': '1000', 'subset': None}

{'func': 'all_less', 'args': ['all_rows', 'enrollment ( 2010 )', '1000'], 'result': True, 'ind': 0, 'tointer': 'for the enrollment ( 2010 ) records of all rows , all of them are less than 1000 .', 'tostr': 'all_less { all_rows ; enrollment ( 2010 ) ; 1000 } = true'}

all_less { all_rows ; enrollment ( 2010 ) ; 1000 } = true

for the enrollment ( 2010 ) records of all rows , all of them are less than 1000 .

1

1

{'all_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'enrollment (2010)_3': 3, '1000_4': 4}

{'all_less_0': 'all_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'enrollment (2010)_3': 'enrollment ( 2010 )', '1000_4': '1000'}

{'all_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'enrollment (2010)_3': [0], '1000_4': [0]}

['school', 'location', 'mascot', 'enrollment ( 2010 )', 'ihsaa class', 'ihsaa football class', 'county']

[['adams central', 'monroe', 'flying jets', '404', 'aa', 'a', '01 adams'], ['bluffton', 'bluffton', 'tigers', '467', 'aa', 'aa', '90 wells'], ['garrett', 'garrett', 'railroaders', '598', 'aaa', 'aaa', '17 de kalb'], ['heritage', 'monroeville', 'patriots', '734', 'aaa', 'aaa', '02 allen'], ['leo', 'leo - cedarville', 'lions', '980', 'aaa', 'aaaa', '02 allen'], ['south adams', 'berne', 'starfires', '398', 'aa', 'a', '01 adams'], ['southern wells', 'poneto', 'raiders', '277', 'a', 'a', '90 wells'], ['woodlan', 'woodburn', 'warriors', '591', 'aaa', 'aa', '02 allen']]

1960 - 61 primeira divisão

https://en.wikipedia.org/wiki/1960%E2%80%9361_Primeira_Divis%C3%A3o

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17933459-2.html.csv

unique

in the 1960 - 61 primeira divisão , for clubs that have 27 seasons at this level , the only one with settlements as porto is the porto club .

{'scope': 'subset', 'row': '4', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'porto', 'subset': {'col': '2', 'criterion': 'equal', 'value': '27 seasons'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'seasons at this level', '27 seasons'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; seasons at this level ; 27 seasons }', 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons .'}, 'settlements', 'porto'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons . among these rows , select the rows whose settlements record fuzzily matches to porto .', 'tostr': 'filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } }', 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons . among these rows , select the rows whose settlements record fuzzily matches to porto . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'seasons at this level', '27 seasons'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; seasons at this level ; 27 seasons }', 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons .'}, 'settlements', 'porto'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons . among these rows , select the rows whose settlements record fuzzily matches to porto .', 'tostr': 'filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto }'}, 'clubs'], 'result': 'porto', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } ; clubs }'}, 'porto'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } ; clubs } ; porto }', 'tointer': 'the clubs record of this unqiue row is porto .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } } ; eq { hop { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } ; clubs } ; porto } } = true', 'tointer': 'select the rows whose seasons at this level record fuzzily matches to 27 seasons . among these rows , select the rows whose settlements record fuzzily matches to porto . there is only one such row in the table . the clubs record of this unqiue row is porto .'}

and { only { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } } ; eq { hop { filter_eq { filter_eq { all_rows ; seasons at this level ; 27 seasons } ; settlements ; porto } ; clubs } ; porto } } = true

select the rows whose seasons at this level record fuzzily matches to 27 seasons . among these rows , select the rows whose settlements record fuzzily matches to porto . there is only one such row in the table . the clubs record of this unqiue row is porto .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'seasons at this level_8': 8, '27 seasons_9': 9, 'settlements_10': 10, 'porto_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'clubs_12': 12, 'porto_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'seasons at this level_8': 'seasons at this level', '27 seasons_9': '27 seasons', 'settlements_10': 'settlements', 'porto_11': 'porto', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'clubs_12': 'clubs', 'porto_13': 'porto'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'seasons at this level_8': [0], '27 seasons_9': [0], 'settlements_10': [1], 'porto_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'clubs_12': [3], 'porto_13': [4]}

['clubs', 'seasons at this level', 'settlements', 'season joined league', 'position in 1959 - 1960']

[['benfica', '27 seasons', 'lisbon', '1934 - 1935', '1'], ['sporting cp', '27 seasons', 'lisbon', '1934 - 1935', '2'], ['belenenses', '27 seasons', 'lisbon', '1934 - 1935', '3'], ['porto', '27 seasons', 'porto', '1934 - 1935', '4'], ['académica de coimbra', '26 seasons', 'coimbra', '1949 - 1950', '6'], ['vitória de guimarães', '17 seasons', 'guimarães', '1958 - 1959', '7'], ['atlético cp', '15 seasons', 'lisbon', '1959 - 1960', '11'], ['barreirense', '14 seasons', 'barreiro', '1960 - 1961', 'segunda divisão'], ['sporting de braga', '13 seasons', 'braga', '1957 - 1958', '12'], ['sporting da covilhã', '12 seasons', 'covilhã', '1958 - 1959', '9'], ['lusitano de évora', '9 seasons', 'évora', '1952 - 1953', '10'], ['cuf barreiro', '8 seasons', 'barreiro', '1954 - 1955', '5'], ['salgueiros', '5 seasons', 'porto', '1960 - 1961', 'segunda divisão'], ['leixões', '4 seasons', 'matosinhos', '1959 - 1960', '8']]

list of ireland cricket captains

https://en.wikipedia.org/wiki/List_of_Ireland_cricket_captains

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11783487-3.html.csv

unique

kyle mccallan is the only player to have a tie in the list of ireland cricket captains .

{'scope': 'all', 'row': '3', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '1', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'tied', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tied record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; tied ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tied ; 1 } }', 'tointer': 'select the rows whose tied record is equal to 1 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'tied', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tied record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; tied ; 1 }'}, 'player'], 'result': 'kyle mccallan', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tied ; 1 } ; player }'}, 'kyle mccallan'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; tied ; 1 } ; player } ; kyle mccallan }', 'tointer': 'the player record of this unqiue row is kyle mccallan .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; tied ; 1 } } ; eq { hop { filter_eq { all_rows ; tied ; 1 } ; player } ; kyle mccallan } } = true', 'tointer': 'select the rows whose tied record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is kyle mccallan .'}

and { only { filter_eq { all_rows ; tied ; 1 } } ; eq { hop { filter_eq { all_rows ; tied ; 1 } ; player } ; kyle mccallan } } = true

select the rows whose tied record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is kyle mccallan .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'tied_7': 7, '1_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'player_9': 9, 'kyle mccallan_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'tied_7': 'tied', '1_8': '1', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'player_9': 'player', 'kyle mccallan_10': 'kyle mccallan'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'tied_7': [0], '1_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'player_9': [2], 'kyle mccallan_10': [3]}

['player', 'dates of captaincy', 'lost', 'tied', 'no result', '% win']

[['alan lewis', '1993 / 94', '4', '0', '0', '42.86'], ['justin benson', '1996 / 97', '3', '0', '1', '66.66'], ['kyle mccallan', '2001 - 2005', '5', '1', '0', '44.44'], ['dekker curry', '2005', '1', '0', '0', '0.00'], ['jason molins', '2005', '0', '0', '0', '100.00'], ['william porterfield', '2009', '2', '0', '0', '80.00'], ['total', '1993 / 94 - 2005', '13', '0', '1', '58.06']]

lindsay davenport career statistics

https://en.wikipedia.org/wiki/Lindsay_Davenport_career_statistics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22858557-1.html.csv

majority

lindsay davenport has played most of her matches on hard surfaces .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'hard', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'hard'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to hard .', 'tostr': 'most_eq { all_rows ; surface ; hard } = true'}

most_eq { all_rows ; surface ; hard } = true

for the surface records of all rows , most of them fuzzily match to hard .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'hard_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'hard_4': 'hard'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'hard_4': [0]}

['outcome', 'year', 'championship', 'surface', 'opponent in final', 'score in final']

[['winner', '1998', 'us open', 'hard', 'martina hingis', '6 - 3 , 7 - 5'], ['winner', '1999', 'wimbledon', 'grass', 'steffi graf', '6 - 4 , 7 - 5'], ['winner', '2000', 'australian open', 'hard', 'martina hingis', '6 - 1 , 7 - 5'], ['runner - up', '2000', 'wimbledon', 'grass', 'venus williams', '6 - 3 , 7 - 6'], ['runner - up', '2000', 'us open', 'hard', 'venus williams', '6 - 4 , 7 - 5'], ['runner - up', '2005', 'australian open', 'hard', 'serena williams', '2 - 6 , 6 - 3 , 6 - 0']]

hunt - class mine countermeasures vessel

https://en.wikipedia.org/wiki/Hunt-class_mine_countermeasures_vessel

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1162013-1.html.csv

majority

the majority of hunt - class mine countermeasures vessels have a home port of portsmouth .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'portsmouth', 'subset': None}

{'func': 'most_str_eq', 'args': ['all_rows', 'home port', 'portsmouth'], 'result': True, 'ind': 0, 'tointer': 'for the home port records of all rows , most of them fuzzily match to portsmouth .', 'tostr': 'most_eq { all_rows ; home port ; portsmouth } = true'}

most_eq { all_rows ; home port ; portsmouth } = true

for the home port records of all rows , most of them fuzzily match to portsmouth .

1

1

{'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'home port_3': 3, 'portsmouth_4': 4}

{'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'home port_3': 'home port', 'portsmouth_4': 'portsmouth'}

{'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'home port_3': [0], 'portsmouth_4': [0]}

['navy', 'name', 'pennant', 'commissioned', 'home port']

[['royal navy', 'brecon', 'm29', '1980', 'hms raleigh'], ['royal navy', 'ledbury', 'm30', '1981', 'portsmouth'], ['royal navy', 'cattistock', 'm31', '1982', 'portsmouth'], ['royal navy', 'cottesmore', 'm32', '1983', 'portsmouth'], ['royal navy', 'brocklesby', 'm33', '1982', 'portsmouth'], ['royal navy', 'middleton', 'm34', '1984', 'portsmouth'], ['royal navy', 'dulverton', 'm35', '1983', 'portsmouth'], ['royal navy', 'bicester', 'm36', '1988', 'portsmouth'], ['royal navy', 'chiddingfold', 'm37', '1984', 'portsmouth'], ['royal navy', 'atherstone', 'm38', '1986', 'portsmouth'], ['royal navy', 'hurworth', 'm39', '1985', 'portsmouth'], ['royal navy', 'berkeley', 'm40', '1986', 'portsmouth'], ['royal navy', 'quorn', 'm41', '1989', 'portsmouth'], ['hellenic navy', 'europa', 'm62', '2001', 'salamis'], ['hellenic navy', 'kallisto', 'm63', '2000', 'salamis'], ['lithuanian naval force', 'skalvis', 'm53', '2011', 'klaipėda'], ['lithuanian naval force', 'kuršis', 'm51', '2011', 'klaipėda']]

euro convergence criteria

https://en.wikipedia.org/wiki/Euro_convergence_criteria

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1884378-1.html.csv

comparative

latvian lats has an earlier official target date than lithuanian litas .

{'row_1': '6', 'row_2': '7', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'currency', 'latvian lats'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose currency record fuzzily matches to latvian lats .', 'tostr': 'filter_eq { all_rows ; currency ; latvian lats }'}, 'official target date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; currency ; latvian lats } ; official target date }', 'tointer': 'select the rows whose currency record fuzzily matches to latvian lats . take the official target date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'currency', 'lithuanian litas'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose currency record fuzzily matches to lithuanian litas .', 'tostr': 'filter_eq { all_rows ; currency ; lithuanian litas }'}, 'official target date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; currency ; lithuanian litas } ; official target date }', 'tointer': 'select the rows whose currency record fuzzily matches to lithuanian litas . take the official target date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; currency ; latvian lats } ; official target date } ; hop { filter_eq { all_rows ; currency ; lithuanian litas } ; official target date } } = true', 'tointer': 'select the rows whose currency record fuzzily matches to latvian lats . take the official target date record of this row . select the rows whose currency record fuzzily matches to lithuanian litas . take the official target date record of this row . the first record is less than the second record .'}

less { hop { filter_eq { all_rows ; currency ; latvian lats } ; official target date } ; hop { filter_eq { all_rows ; currency ; lithuanian litas } ; official target date } } = true

select the rows whose currency record fuzzily matches to latvian lats . take the official target date record of this row . select the rows whose currency record fuzzily matches to lithuanian litas . take the official target date record of this row . the first record is less than the second record .

5

5

{'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'currency_7': 7, 'latvian lats_8': 8, 'official target date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'currency_11': 11, 'lithuanian litas_12': 12, 'official target date_13': 13}

{'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'currency_7': 'currency', 'latvian lats_8': 'latvian lats', 'official target date_9': 'official target date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'currency_11': 'currency', 'lithuanian litas_12': 'lithuanian litas', 'official target date_13': 'official target date'}

{'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'currency_7': [0], 'latvian lats_8': [0], 'official target date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'currency_11': [1], 'lithuanian litas_12': [1], 'official target date_13': [3]}

['currency', 'code', 'entry erm ii', 'central rate', 'official target date']

[['bulgarian lev', 'bgn', '-', '1.95583', '-'], ['croatian kuna', 'hrk', '-', '-', '-'], ['czech koruna', 'czk', '-', '-', '-'], ['danish krone', 'dkk', '1 january 1999', '7.46038', 'formal opt - out'], ['hungarian forint', 'huf', '-', '-', '-'], ['latvian lats', 'lvl', '2 may 2005', '0.702804', '1 january 2014'], ['lithuanian litas', 'ltl', '28 june 2004', '3.45280', '1 january 2015'], ['polish złoty', 'pln', '-', '-', '-'], ['romanian leu', 'ron', '-', '-', '-'], ['swedish krona', 'sek', 'not considered', '-', 'de facto opt - out'], ['british pound sterling gibraltar pound', 'gbp gip', 'not considered', '-', 'formal opt - out']]

list of highest - grossing bollywood films

https://en.wikipedia.org/wiki/List_of_highest-grossing_Bollywood_films

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11872185-6.html.csv

majority

most of the top ten grossing films made over 25,00,000 in a single day .

{'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '25 , 00 , 00000', 'subset': None}

{'func': 'most_greater', 'args': ['all_rows', 'single day net gross', '25 , 00 , 00000'], 'result': True, 'ind': 0, 'tointer': 'for the single day net gross records of all rows , most of them are greater than 25 , 00 , 00000 .', 'tostr': 'most_greater { all_rows ; single day net gross ; 25 , 00 , 00000 } = true'}

most_greater { all_rows ; single day net gross ; 25 , 00 , 00000 } = true

for the single day net gross records of all rows , most of them are greater than 25 , 00 , 00000 .

1

1

{'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'single day net gross_3': 3, '25 , 00 , 00000_4': 4}

{'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'single day net gross_3': 'single day net gross', '25 , 00 , 00000_4': '25 , 00 , 00000'}

{'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'single day net gross_3': [0], '25 , 00 , 00000_4': [0]}

['rank', 'movie', 'year', 'studio ( s )', 'single day net gross', 'day in release', 'day of week']

[['1', 'chennai express', '2013', 'red chillies entertainment', '33 , 12 , 00000', 'friday', '1'], ['2', 'ek tha tiger', '2012', 'yash raj films', '32 , 93 , 00000', 'wednesday', '1'], ['3', 'chennai express', '2013', 'red chillies entertainment', '32 , 50 , 00000', 'sunday', '3'], ['4', 'chennai express', '2013', 'red chillies entertainment', '28 , 50 , 00000', 'saturday', '2'], ['5', 'dabangg 2', '2012', 'arbaaz khan productions', '25 , 50 , 00000', 'sunday', '3'], ['6', 'raone', '2011', 'red chillies entertainment', '25 , 00 , 00000', 'friday', '2'], ['7', 'yeh jawaani hai deewani', '2013', 'dharma productions', '22 , 69 , 00000', 'sunday', '3'], ['8', 'agneepath', '2012', 'dharma productions', '21 , 72 , 00000', 'thursday', '1'], ['9', 'bodyguard', '2011', 'reliance entertainment', '20 , 66 , 00000', 'wednesday', '1'], ['10', 'yeh jawaani hai deewani', '2013', 'dharma productions', '20 , 50 , 00000', 'saturday', '2']]

nadia fanchini

https://en.wikipedia.org/wiki/Nadia_Fanchini

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15692953-1.html.csv

unique

nadia fanchini only placed first one time in lake louise , canada on december 7 , 2008 .

{'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': '1st', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'place', '1st'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place record fuzzily matches to 1st .', 'tostr': 'filter_eq { all_rows ; place ; 1st }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; place ; 1st } }', 'tointer': 'select the rows whose place record fuzzily matches to 1st . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'place', '1st'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose place record fuzzily matches to 1st .', 'tostr': 'filter_eq { all_rows ; place ; 1st }'}, 'date'], 'result': '7 dec 2008', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; place ; 1st } ; date }'}, '7 dec 2008'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; place ; 1st } ; date } ; 7 dec 2008 }', 'tointer': 'the date record of this unqiue row is 7 dec 2008 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; place ; 1st } } ; eq { hop { filter_eq { all_rows ; place ; 1st } ; date } ; 7 dec 2008 } } = true', 'tointer': 'select the rows whose place record fuzzily matches to 1st . there is only one such row in the table . the date record of this unqiue row is 7 dec 2008 .'}

and { only { filter_eq { all_rows ; place ; 1st } } ; eq { hop { filter_eq { all_rows ; place ; 1st } ; date } ; 7 dec 2008 } } = true

select the rows whose place record fuzzily matches to 1st . there is only one such row in the table . the date record of this unqiue row is 7 dec 2008 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'place_7': 7, '1st_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '7 dec 2008_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'place_7': 'place', '1st_8': '1st', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '7 dec 2008_10': '7 dec 2008'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'place_7': [0], '1st_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '7 dec 2008_10': [3]}

['season', 'date', 'location', 'discipline', 'place']

[['2007', '1 dec 2006', 'lake louise , canada', 'downhill', '3rd'], ['2008', '9 feb 2008', 'sestriere , italy', 'downhill', '3rd'], ['2008', '8 mar 2008', 'crans montana , switzerland', 'downhill', '3rd'], ['2009', '5 dec 2008', 'lake louise , canada', 'downhill', '2nd'], ['2009', '7 dec 2008', 'lake louise , canada', 'super g', '1st'], ['2009', '20 dec 2008', 'st moritz , switzerland', 'super g', '3rd'], ['2009', '27 feb 2009', 'bansko , bulgaria', 'downhill', '3rd'], ['2009', '10 mar 2009', 'ã … re , sweden', 'super g', '2nd'], ['2010', '10 jan 2010', 'haus im ennstal , austria', 'super g', '3rd']]

art competitions at the 1928 summer olympics

https://en.wikipedia.org/wiki/Art_competitions_at_the_1928_Summer_Olympics

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16574447-6.html.csv

aggregation

in art competitions at the 1928 summer olympics , the average number of gold medals won was .82 .

{'scope': 'all', 'col': '3', 'type': 'average', 'result': '.82', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'gold'], 'result': '.82', 'ind': 0, 'tostr': 'avg { all_rows ; gold }'}, '.82'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; gold } ; .82 } = true', 'tointer': 'the average of the gold record of all rows is .82 .'}

round_eq { avg { all_rows ; gold } ; .82 } = true

the average of the gold record of all rows is .82 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'gold_4': 4, '.82_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '.82_5': '.82'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'gold_4': [0], '.82_5': [1]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'netherlands ( ned )', '2', '1', '1', '4'], ['2', 'germany ( ger )', '1', '2', '5', '8'], ['3', 'france ( fra )', '1', '2', '1', '4'], ['4', 'great britain ( gbr )', '1', '1', '0', '2'], ['5', 'poland ( pol )', '1', '0', '1', '2'], ['6', 'austria ( aut )', '1', '0', '0', '1'], ['6', 'hungary ( hun )', '1', '0', '0', '1'], ['6', 'luxembourg ( lux )', '1', '0', '0', '1'], ['9', 'switzerland ( sui )', '0', '2', '0', '2'], ['10', 'denmark ( den )', '0', '1', '2', '3'], ['11', 'italy ( ita )', '0', '1', '0', '1']]

camarines norte

https://en.wikipedia.org/wiki/Camarines_Norte

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-255885-1.html.csv

comparative

in camarines norte , jose panganiban has a larger area than daet .

{'row_1': '4', 'row_2': '3', 'col': '3', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'municipality', 'jose panganiban'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose municipality record fuzzily matches to jose panganiban .', 'tostr': 'filter_eq { all_rows ; municipality ; jose panganiban }'}, 'area ( km square )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; municipality ; jose panganiban } ; area ( km square ) }', 'tointer': 'select the rows whose municipality record fuzzily matches to jose panganiban . take the area ( km square ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'municipality', 'daet ( capital town )'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose municipality record fuzzily matches to daet ( capital town ) .', 'tostr': 'filter_eq { all_rows ; municipality ; daet ( capital town ) }'}, 'area ( km square )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; municipality ; daet ( capital town ) } ; area ( km square ) }', 'tointer': 'select the rows whose municipality record fuzzily matches to daet ( capital town ) . take the area ( km square ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; municipality ; jose panganiban } ; area ( km square ) } ; hop { filter_eq { all_rows ; municipality ; daet ( capital town ) } ; area ( km square ) } } = true', 'tointer': 'select the rows whose municipality record fuzzily matches to jose panganiban . take the area ( km square ) record of this row . select the rows whose municipality record fuzzily matches to daet ( capital town ) . take the area ( km square ) record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; municipality ; jose panganiban } ; area ( km square ) } ; hop { filter_eq { all_rows ; municipality ; daet ( capital town ) } ; area ( km square ) } } = true

select the rows whose municipality record fuzzily matches to jose panganiban . take the area ( km square ) record of this row . select the rows whose municipality record fuzzily matches to daet ( capital town ) . take the area ( km square ) record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'municipality_7': 7, 'jose panganiban_8': 8, 'area (km square)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'municipality_11': 11, 'daet (capital town)_12': 12, 'area (km square)_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'municipality_7': 'municipality', 'jose panganiban_8': 'jose panganiban', 'area (km square)_9': 'area ( km square )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'municipality_11': 'municipality', 'daet (capital town)_12': 'daet ( capital town )', 'area (km square)_13': 'area ( km square )'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'municipality_7': [0], 'jose panganiban_8': [0], 'area (km square)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'municipality_11': [1], 'daet (capital town)_12': [1], 'area (km square)_13': [3]}

['municipality', 'no of s barangay', 'area ( km square )', 'population ( 2007 )', 'population ( 2010 )']

[['basud', '29', '260.28', '36763', '38176'], ['capalonga', '22', '290.00', '29683', '31299'], ['daet ( capital town )', '25', '46.00', '94184', '95572'], ['jose panganiban', '27', '214.44', '49028', '55557'], ['labo', '52', '589.36', '88087', '92041'], ['mercedes', '26', '173.69', '44375', '47674'], ['paracale', '27', '197.90', '46856', '53243'], ['san lorenzo ruiz', '12', '119.37', '12299', '12592'], ['san vicente', '9', '57.49', '9615', '10114'], ['santa elena', '19', '199.35', '40300', '40828'], ['talisay', '15', '30.76', '22942', '23904']]

westinghouse broadcasting

https://en.wikipedia.org/wiki/Westinghouse_Broadcasting

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1553485-1.html.csv

unique

for westinghouse broadcasting , when the current affiliation is cbs owned and operated , the only time the city is pittsburgh is when the station is kdka-tv .

{'scope': 'subset', 'row': '7', 'col': '1', 'col_other': '2', 'criterion': 'equal', 'value': 'pittsburgh', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'cbs owned - and - operated ( o & o )'}}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'current affiliation', 'cbs owned - and - operated ( o & o )'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) }', 'tointer': 'select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) .'}, 'city of license / market', 'pittsburgh'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) . among these rows , select the rows whose city of license / market record fuzzily matches to pittsburgh .', 'tostr': 'filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } }', 'tointer': 'select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) . among these rows , select the rows whose city of license / market record fuzzily matches to pittsburgh . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'current affiliation', 'cbs owned - and - operated ( o & o )'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) }', 'tointer': 'select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) .'}, 'city of license / market', 'pittsburgh'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) . among these rows , select the rows whose city of license / market record fuzzily matches to pittsburgh .', 'tostr': 'filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh }'}, 'station'], 'result': 'kdka - tv', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } ; station }'}, 'kdka - tv'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } ; station } ; kdka - tv }', 'tointer': 'the station record of this unqiue row is kdka - tv .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } } ; eq { hop { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } ; station } ; kdka - tv } } = true', 'tointer': 'select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) . among these rows , select the rows whose city of license / market record fuzzily matches to pittsburgh . there is only one such row in the table . the station record of this unqiue row is kdka - tv .'}

and { only { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } } ; eq { hop { filter_eq { filter_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) } ; city of license / market ; pittsburgh } ; station } ; kdka - tv } } = true

select the rows whose current affiliation record fuzzily matches to cbs owned - and - operated ( o & o ) . among these rows , select the rows whose city of license / market record fuzzily matches to pittsburgh . there is only one such row in the table . the station record of this unqiue row is kdka - tv .

8

6

{'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'current affiliation_8': 8, 'cbs owned - and - operated (o&o)_9': 9, 'city of license / market_10': 10, 'pittsburgh_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'station_12': 12, 'kdka - tv_13': 13}

{'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'current affiliation_8': 'current affiliation', 'cbs owned - and - operated (o&o)_9': 'cbs owned - and - operated ( o & o )', 'city of license / market_10': 'city of license / market', 'pittsburgh_11': 'pittsburgh', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'station_12': 'station', 'kdka - tv_13': 'kdka - tv'}

{'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'current affiliation_8': [0], 'cbs owned - and - operated (o&o)_9': [0], 'city of license / market_10': [1], 'pittsburgh_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'station_12': [3], 'kdka - tv_13': [4]}

['city of license / market', 'station', 'channel tv ( dt )', 'years owned', 'current affiliation']

[['san francisco - oakland - san jose', 'kpix', '5 ( 29 )', '1954 - 1995', 'cbs owned - and - operated ( o & o )'], ['baltimore', 'wjz - tv', '13 ( 13 )', '1957 - 1995', 'cbs owned - and - operated ( o & o )'], ['boston', 'wbz - tv', '4 ( 30 )', '1948 - 1995', 'cbs owned - and - operated ( o & o )'], ['charlotte', 'wpcq - tv ( now wcnc - tv )', '36 ( 22 )', '1980 - 1985', 'nbc affiliate owned by belo corporation'], ['cleveland', 'kyw - tv ( now wkyc - tv )', '3 ( 17 )', '1956 - 1965', 'nbc affiliate owned by gannett company'], ['philadelphia', 'wptz / kyw - tv', '3 ( 26 )', '1953 - 1956 1965 - 1995', 'cbs owned - and - operated ( o & o )'], ['pittsburgh', 'kdka - tv', '2 ( 25 )', '1955 - 1995', 'cbs owned - and - operated ( o & o )']]

richard crunkilton

https://en.wikipedia.org/wiki/Richard_Crunkilton

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17443070-2.html.csv

aggregation

the average number of rounds played by richard crunkilton during an event was 1.95 rounds .

{'scope': 'all', 'col': '6', 'type': 'average', 'result': '1.95', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'round'], 'result': '1.95', 'ind': 0, 'tostr': 'avg { all_rows ; round }'}, '1.95'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; round } ; 1.95 } = true', 'tointer': 'the average of the round record of all rows is 1.95 .'}

round_eq { avg { all_rows ; round } ; 1.95 } = true

the average of the round record of all rows is 1.95 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'round_4': 4, '1.95_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'round_4': 'round', '1.95_5': '1.95'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'round_4': [0], '1.95_5': [1]}

['res', 'record', 'opponent', 'method', 'event', 'round', 'time']

[['win', '17 - 3', 'carlo prater', 'decision ( split )', 'shine : lightweight grand prix', '3', '5:00'], ['loss', '16 - 3', 'dave jansen', 'decision ( unanimous )', 'wec 43', '3', '5:00'], ['win', '16 - 2', 'sergio gomez', 'decision ( unanimous )', 'wec 33', '3', '5:00'], ['loss', '15 - 2', 'rob mccullough', 'tko ( punches )', 'wec 30', '1', '1:29'], ['win', '15 - 1', 'mike joy', 'submission ( brabo choke )', 'wec 25', '3', '4:23'], ['win', '14 - 1', 'adam lynn', 'submission ( rear naked choke )', 'wec 21', '2', '1:20'], ['win', '13 - 1', 'nick ertl', 'tko ( strikes )', 'wec 18', '2', '3:55'], ['win', '12 - 1', 'james martinez', 'tko', 'freedom fight - canada vs usa', '1', '3:31'], ['win', '11 - 1', 'paul jenkins', 'ko', 'wec 15', '2', '2:36'], ['win', '10 - 1', 'peter kaljevic', 'submission ( armbar )', 'rfc 1 - real fighting championships 1', '1', '3:44'], ['loss', '9 - 1', 'hermes frança', 'decision ( unanimous )', 'ufc 42', '3', '5:00'], ['win', '9 - 0', 'víctor estrada', 'submission ( ankle injury )', 'wec 5', '1', '1:13'], ['win', '8 - 0', 'luciano oliveira', 'submission ( armbar )', 'wec 4', '1', '1:55'], ['win', '7 - 0', 'cruz gomez', 'tko', 'wec 3', '1', '3:04'], ['win', '6 - 0', 'bao quach', 'ko', 'ua 2 - the gathering', '2', '1:20'], ['win', '5 - 0', 'aaron jerome', 'tko', 'ritr - rumble in the rockies', '1', '1:00'], ['win', '4 - 0', 'eric hibler', 'decision', 'rsf 1 - redemption in the valley', '3', '4:00'], ['win', '3 - 0', 'scott johnson', 'submission ( knee bar )', 'wvf - battlejax', '1', '5:19'], ['win', '2 - 0', 'robert irizarry', 'decision ( unanimous )', 'wef - new blood conflict', '3', '3:00'], ['win', '1 - 0', 'ray totorico', 'tko ( strikes )', 'wef 7 - stomp in the swamp', '2', '2:24']]

the apprentice australia

https://en.wikipedia.org/wiki/The_Apprentice_Australia

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24501530-1.html.csv

aggregation

the average age of candidates on the apprentice australia was 30 years old .

{'scope': 'all', 'col': '4', 'type': 'average', 'result': '30', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'age'], 'result': '30', 'ind': 0, 'tostr': 'avg { all_rows ; age }'}, '30'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; age } ; 30 } = true', 'tointer': 'the average of the age record of all rows is 30 .'}

round_eq { avg { all_rows ; age } ; 30 } = true

the average of the age record of all rows is 30 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'age_4': 4, '30_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'age_4': 'age', '30_5': '30'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'age_4': [0], '30_5': [1]}

['candidate', 'background', 'original team', 'age', 'hometown', 'result']

[['andrew morello morello', 'auctioneer', 'pinnacle', '23', 'melbourne , victoria', 'hired by bouris'], ['heather williams', 'advertising sales consultant', 'eventus', '31', 'maylands , western australia', 'fired 2nd in finale'], ['gavin mcinnes', 'lawyer', 'pinnacle', '33', 'brisbane , queensland', 'fired 1st in finale'], ['mary - anne lowe', 'business owner', 'eventus', '30', 'melbourne , victoria', 'fired in week 9'], ['sabrina houssami', 'university student and miss world australia 2006', 'eventus', '23', 'sydney , new south wales', 'fired in week 8'], ['samuel sam hooper', 'law student', 'pinnacle', '19', 'adelaide , south australia', 'fired in week 7'], ['carmen parnos', 'bankrupt former entrepreneur', 'eventus', '44', 'melbourne , victoria', 'fired in week 6'], ['john van yzerloo', 'unemployed', 'pinnacle', '44', 'romsey , victoria', 'fired in week 5'], ['blake chandler', 'customer service manager', 'pinnacle', '28', 'central coast , new south wales', 'fired in week 4'], ['amy cato', 'business owner', 'eventus', '25', 'adelaide , south australia', 'fired in week 3'], ['lynton pipkorn', 'marketing consultant', 'pinnacle', '30', 'melbourne , victoria', 'fired in week 2']]

2007 - 08 dallas stars season

https://en.wikipedia.org/wiki/2007%E2%80%9308_Dallas_Stars_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11801912-4.html.csv

majority

all games of the dallas stars ' in the 2007 - 08 season were played in the month of november .

{'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'november', 'subset': None}

{'func': 'all_str_eq', 'args': ['all_rows', 'date', 'november'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to november .', 'tostr': 'all_eq { all_rows ; date ; november } = true'}

all_eq { all_rows ; date ; november } = true

for the date records of all rows , all of them fuzzily match to november .

1

1

{'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'november_4': 4}

{'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'november_4': 'november'}

{'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'november_4': [0]}

['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record']

[['november 2', 'phoenix', '5 - 0', 'dallas', 'smith', '18203', '5 - 6 - 2'], ['november 5', 'dallas', '5 - 0', 'anaheim', 'turco', '17174', '6 - 6 - 2'], ['november 7', 'dallas', '3 - 1', 'san jose', 'turco', '17496', '7 - 6 - 2'], ['november 8', 'dallas', '2 - 5', 'phoenix', 'turco', '12027', '7 - 7 - 2'], ['november 10', 'dallas', '5 - 6', 'los angeles', 'turco', '18118', '7 - 7 - 3'], ['november 14', 'san jose', '4 - 3', 'dallas', 'turco', '17682', '7 - 7 - 4'], ['november 16', 'colorado', '1 - 6', 'dallas', 'smith', '18019', '8 - 7 - 4'], ['november 19', 'los angeles', '0 - 3', 'dallas', 'smith', '17208', '9 - 7 - 4'], ['november 21', 'anaheim', '1 - 2', 'dallas', 'smith', '18584', '10 - 7 - 4'], ['november 23', 'toronto', '1 - 3', 'dallas', 'turco', '18409', '11 - 7 - 4'], ['november 25', 'dallas', '3 - 2', 'ny rangers', 'smith', '18200', '12 - 7 - 4'], ['november 26', 'dallas', '3 - 2', 'ny islanders', 'turco', '8161', '13 - 7 - 4'], ['november 28', 'dallas', '2 - 4', 'new jersey', 'turco', '13665', '13 - 8 - 4'], ['november 30', 'dallas', '1 - 4', 'pittsburgh', 'smith', '17132', '13 - 9 - 4']]

kashmir

https://en.wikipedia.org/wiki/Kashmir

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17337-1.html.csv

unique

ladakh area , is the only area that has any practicing buddhist .

{'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'not_equal', 'value': '-', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', '% buddhist', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose % buddhist record is not equal to - .', 'tostr': 'filter_not_eq { all_rows ; % buddhist ; - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_not_eq { all_rows ; % buddhist ; - } }', 'tointer': 'select the rows whose % buddhist record is not equal to - . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_not_eq', 'args': ['all_rows', '% buddhist', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose % buddhist record is not equal to - .', 'tostr': 'filter_not_eq { all_rows ; % buddhist ; - }'}, 'area'], 'result': 'ladakh', 'ind': 2, 'tostr': 'hop { filter_not_eq { all_rows ; % buddhist ; - } ; area }'}, 'ladakh'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_not_eq { all_rows ; % buddhist ; - } ; area } ; ladakh }', 'tointer': 'the area record of this unqiue row is ladakh .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_not_eq { all_rows ; % buddhist ; - } } ; eq { hop { filter_not_eq { all_rows ; % buddhist ; - } ; area } ; ladakh } } = true', 'tointer': 'select the rows whose % buddhist record is not equal to - . there is only one such row in the table . the area record of this unqiue row is ladakh .'}

and { only { filter_not_eq { all_rows ; % buddhist ; - } } ; eq { hop { filter_not_eq { all_rows ; % buddhist ; - } ; area } ; ladakh } } = true

select the rows whose % buddhist record is not equal to - . there is only one such row in the table . the area record of this unqiue row is ladakh .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_not_eq_0': 0, 'all_rows_6': 6, '% buddhist_7': 7, '-_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'area_9': 9, 'ladakh_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_not_eq_0': 'filter_not_eq', 'all_rows_6': 'all_rows', '% buddhist_7': '% buddhist', '-_8': '-', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'area_9': 'area', 'ladakh_10': 'ladakh'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_not_eq_0': [1, 2], 'all_rows_6': [0], '% buddhist_7': [0], '-_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'area_9': [2], 'ladakh_10': [3]}

['area', 'population', '% muslim', '% hindu', '% buddhist', '% other']

[['kashmir valley', '~ 4 million ( 4 million )', '95 %', '4 %', '-', '-'], ['jammu', '~ 3 million ( 3 million )', '30 %', '66 %', '-', '4 %'], ['ladakh', '~ 0.25 million ( 250000 )', '46 %', '-', '50 %', '3 %'], ['azad kashmir', '~ 2.6 million ( 2.6 million )', '100 %', '-', '-', '-'], ['gilgit - baltistan', '~ 1 million ( 1 million )', '99 %', '-', '-', '-'], ['aksai chin', '-', '-', '-', '-', '-']]

50 metre rifle prone

https://en.wikipedia.org/wiki/50_metre_rifle_prone

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18656178-1.html.csv

count

in the 50 metre rifle prone , between year 1962 and 1974 it was once in cairo .

{'scope': 'subset', 'criterion': 'equal', 'value': 'cairo', 'result': '1', 'col': '2', 'subset': {'col': '1', 'criterion': 'less_than_eq', 'value': '1974'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'year', '1974'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; year ; 1974 }', 'tointer': 'select the rows whose year record is less than or equal to 1974 .'}, 'place', 'cairo'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo .', 'tostr': 'filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo }'}], 'result': '1', 'ind': 2, 'tostr': 'count { filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo } }', 'tointer': 'select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo } } ; 1 } = true', 'tointer': 'select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo . the number of such rows is 1 .'}

eq { count { filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo } } ; 1 } = true

select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo . the number of such rows is 1 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_less_eq_0': 0, 'all_rows_5': 5, 'year_6': 6, '1974_7': 7, 'place_8': 8, 'cairo_9': 9, '1_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_5': 'all_rows', 'year_6': 'year', '1974_7': '1974', 'place_8': 'place', 'cairo_9': 'cairo', '1_10': '1'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_less_eq_0': [1], 'all_rows_5': [0], 'year_6': [0], '1974_7': [0], 'place_8': [1], 'cairo_9': [1], '1_10': [3]}

['year', 'place', 'gold', 'silver', 'bronze']

[['1962', 'cairo', 'karl wenk ( frg )', 'vladimir chuian ( urs )', 'james enoch hill ( usa )'], ['1966', 'wiesbaden', 'david boyd ( usa )', 'jerzy nowicki ( pol )', 'bill krilling ( usa )'], ['1970', 'phoenix', 'manfred fiess ( rsa )', 'esa einari kervinen ( fin )', 'klaus zaehringer ( frg )'], ['1974', 'thun', 'karel bulan ( tch )', 'helge edvin anshushaug ( nor )', 'wolfram waibel sr ( aut )'], ['1978', 'seoul', 'alister allan ( gbr )', 'lones wigger ( usa )', 'lanny bassham ( usa )'], ['1982', 'caracas', 'victor daniltchenko ( urs )', 'william beard ( usa )', 'viktor vlasov ( urs )'], ['1986', 'suhl', 'sandor bereczky ( hun )', 'gale stewart ( can )', 'michael heine ( frg )'], ['1990', 'moscow', 'viatcheslav botchkarev ( urs )', 'harald stenvaag ( nor )', 'tadeusz czerwinski ( pol )'], ['1994', 'milan', 'wenjie li ( chn )', 'stevan pletikosic ( iop )', 'michel bury ( fra )'], ['1998', 'barcelona', 'thomas tamas ( usa )', 'juha hirvi ( fin )', 'sergei kovalenko ( rus )'], ['2002', 'lahti', 'matthew emmons ( usa )', 'rajmond debevec ( slo )', 'espen berg - knutsen ( nor )'], ['2006', 'zagreb', 'sergei martynov ( blr )', 'jury sukhorukov ( ukr )', 'marco de nicolo ( ita )']]

mont ventoux

https://en.wikipedia.org/wiki/Mont_Ventoux

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-162439-2.html.csv

unique

1974 was the only year that gonzalo aja was the leader at the summit of the mont ventoux race .

{'scope': 'all', 'row': '2', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': 'gonzalo aja', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leader at the summit', 'gonzalo aja'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose leader at the summit record fuzzily matches to gonzalo aja .', 'tostr': 'filter_eq { all_rows ; leader at the summit ; gonzalo aja }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; leader at the summit ; gonzalo aja } }', 'tointer': 'select the rows whose leader at the summit record fuzzily matches to gonzalo aja . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leader at the summit', 'gonzalo aja'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose leader at the summit record fuzzily matches to gonzalo aja .', 'tostr': 'filter_eq { all_rows ; leader at the summit ; gonzalo aja }'}, 'year'], 'result': '1974', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; leader at the summit ; gonzalo aja } ; year }'}, '1974'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; leader at the summit ; gonzalo aja } ; year } ; 1974 }', 'tointer': 'the year record of this unqiue row is 1974 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; leader at the summit ; gonzalo aja } } ; eq { hop { filter_eq { all_rows ; leader at the summit ; gonzalo aja } ; year } ; 1974 } } = true', 'tointer': 'select the rows whose leader at the summit record fuzzily matches to gonzalo aja . there is only one such row in the table . the year record of this unqiue row is 1974 .'}

and { only { filter_eq { all_rows ; leader at the summit ; gonzalo aja } } ; eq { hop { filter_eq { all_rows ; leader at the summit ; gonzalo aja } ; year } ; 1974 } } = true

select the rows whose leader at the summit record fuzzily matches to gonzalo aja . there is only one such row in the table . the year record of this unqiue row is 1974 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'leader at the summit_7': 7, 'gonzalo aja_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1974_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'leader at the summit_7': 'leader at the summit', 'gonzalo aja_8': 'gonzalo aja', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1974_10': '1974'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'leader at the summit_7': [0], 'gonzalo aja_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1974_10': [3]}

['year', 'stage', 'category', 'start', 'finish', 'leader at the summit']

[['1994', '15', 'hc', 'montpellier', 'carpentras', 'eros poli ( ita )'], ['1974', '12', '1', 'savines - le - lac', 'orange', 'gonzalo aja ( esp )'], ['1967', '13', '1', 'marseille', 'carpentras', 'julio jimãnez ( esp )'], ['1955', '11', '1', 'marseille', 'avignon', 'louison bobet ( fra )'], ['1952', '14', '1', 'aix - en - provence', 'avignon', 'jean robic ( fra )'], ['1951', '18', '1', 'montpellier', 'avignon', 'lucien lazarides ( fra )']]

2008 in british television

https://en.wikipedia.org/wiki/2008_in_British_television

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13549921-18.html.csv

count

three of the programmes returned on the same channel as the original .

{'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'same channel as original', 'result': '3', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'new channel ( s )', 'same channel as original'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose new channel ( s ) record fuzzily matches to same channel as original .', 'tostr': 'filter_eq { all_rows ; new channel ( s ) ; same channel as original }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; new channel ( s ) ; same channel as original } }', 'tointer': 'select the rows whose new channel ( s ) record fuzzily matches to same channel as original . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; new channel ( s ) ; same channel as original } } ; 3 } = true', 'tointer': 'select the rows whose new channel ( s ) record fuzzily matches to same channel as original . the number of such rows is 3 .'}

eq { count { filter_eq { all_rows ; new channel ( s ) ; same channel as original } } ; 3 } = true

select the rows whose new channel ( s ) record fuzzily matches to same channel as original . the number of such rows is 3 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'new channel (s)_5': 5, 'same channel as original_6': 6, '3_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'new channel (s)_5': 'new channel ( s )', 'same channel as original_6': 'same channel as original', '3_7': '3'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'new channel (s)_5': [0], 'same channel as original_6': [0], '3_7': [2]}

['programme', 'date ( s ) of original removal', 'original channel', 'date ( s ) of return', 'new channel ( s )']

[['mr and mrs as all star mr & mrs', '1999', 'itv', '12 april 2008', 'n / a ( same channel as original )'], ['itv news at ten', '5 march 1999 30 january 2004', 'itv', '22 january 2001 14 january 2008', 'n / a ( same channel as original )'], ['gladiators', '1 january 2000', 'itv', '11 may 2008', 'sky1'], ['superstars', '2005', 'bbc one', 'july 2008', 'five'], ["it 'll be alright on the night", '18 march 2006', 'itv', '20 september 2008', 'n / a ( same channel as original )']]

1948 vfl season

https://en.wikipedia.org/wiki/1948_VFL_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10809529-8.html.csv

superlative

in the 1948 vfl season , the match that took place at punt road oval had the largest crowd .

{'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'punt road oval', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'punt road oval'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is punt road oval .'}

eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval } = true

select the row whose crowd record of all rows is maximum . the venue record of this row is punt road oval .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'punt road oval_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'punt road oval_7': 'punt road oval'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'punt road oval_7': [2]}

['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date']

[['geelong', '15.16 ( 106 )', 'south melbourne', '19.12 ( 126 )', 'kardinia park', '19500', '5 june 1948'], ['collingwood', '11.17 ( 83 )', 'melbourne', '11.10 ( 76 )', 'victoria park', '20000', '5 june 1948'], ['st kilda', '7.12 ( 54 )', 'hawthorn', '10.12 ( 72 )', 'junction oval', '7000', '5 june 1948'], ['north melbourne', '11.6 ( 72 )', 'footscray', '8.9 ( 57 )', 'arden street oval', '12000', '5 june 1948'], ['fitzroy', '12.8 ( 80 )', 'essendon', '9.10 ( 64 )', 'brunswick street oval', '25000', '5 june 1948'], ['richmond', '13.10 ( 88 )', 'carlton', '9.21 ( 75 )', 'punt road oval', '33000', '5 june 1948']]

sri lanka at the commonwealth games

https://en.wikipedia.org/wiki/Sri_Lanka_at_the_Commonwealth_Games

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18916531-3.html.csv

count

a total of six sri lankan people won silver medals at the commonwealth games .

{'scope': 'all', 'criterion': 'equal', 'value': 'silver', 'result': '6', 'col': '1', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'medal', 'silver'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose medal record fuzzily matches to silver .', 'tostr': 'filter_eq { all_rows ; medal ; silver }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; medal ; silver } }', 'tointer': 'select the rows whose medal record fuzzily matches to silver . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; medal ; silver } } ; 6 } = true', 'tointer': 'select the rows whose medal record fuzzily matches to silver . the number of such rows is 6 .'}

eq { count { filter_eq { all_rows ; medal ; silver } } ; 6 } = true

select the rows whose medal record fuzzily matches to silver . the number of such rows is 6 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'medal_5': 5, 'silver_6': 6, '6_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'medal_5': 'medal', 'silver_6': 'silver', '6_7': '6'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'medal_5': [0], 'silver_6': [0], '6_7': [2]}

['medal', 'name', 'games', 'sport', 'event']

[['gold', 'barney henricus', '1938 sydney', 'boxing', 'featherweight ( 57 kg )'], ['gold', 'duncan white', '1950 auckland', 'athletics', "men 's 440 yards hurdles"], ['gold', 'pushpamali ramanayake malee wickremasinghe', '1994 victoria', 'shooting', "women 's air rifle - pairs"], ['gold', 'chinthana vidanage', '2006 melbourne', 'weightlifting', "men 's 62 kg"], ['silver', 'k edwin', '1950 auckland', 'boxing', 'flyweight'], ['silver', 'albert perera', '1950 auckland', 'boxing', 'bantamweight'], ['silver', 'dodangoda chandrasiri lakshman rajasinghe', '1994 victoria', 'shooting', "men 's small bore rifle , prone - pairs"], ['silver', 'malee wickremasinghe', '1994 victoria', 'shooting', "women 's air rifle"], ['silver', 'sriyani kulawansha', '1998 kuala lumpur', 'athletics', "women 's 100 m hurdles"], ['silver', 'chinthana vidanage', '2010 delhi', 'weightlifting', "men 's 69 kg"], ['bronze', 'alex obeyesekera', '1950 auckland', 'boxing', 'welterweight'], ['bronze', 'sugath thilakaratne', '1998 kuala lumpur', 'athletics', "men 's 400 m"], ['bronze', 'sudesh peiris', '2010 deilh', 'weightlifting', "men 's 62 kg"]]

1986 dallas cowboys season

https://en.wikipedia.org/wiki/1986_Dallas_Cowboys_season

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11309481-2.html.csv

comparative

more people attended the first game of the dallas cowboys ' season in 1986 than the last game .

{'row_1': '1', 'row_2': '16', 'col': '6', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None}

{'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september 8 , 1986'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to september 8 , 1986 .', 'tostr': 'filter_eq { all_rows ; date ; september 8 , 1986 }'}, 'attendance'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december 21 , 1986'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to december 21 , 1986 .', 'tostr': 'filter_eq { all_rows ; date ; december 21 , 1986 }'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance }', 'tointer': 'select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance } } = true', 'tointer': 'select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row . the first record is greater than the second record .'}

greater { hop { filter_eq { all_rows ; date ; september 8 , 1986 } ; attendance } ; hop { filter_eq { all_rows ; date ; december 21 , 1986 } ; attendance } } = true

select the rows whose date record fuzzily matches to september 8 , 1986 . take the attendance record of this row . select the rows whose date record fuzzily matches to december 21 , 1986 . take the attendance record of this row . the first record is greater than the second record .

5

5

{'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'date_7': 7, 'september 8 , 1986_8': 8, 'attendance_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'december 21 , 1986_12': 12, 'attendance_13': 13}

{'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'date_7': 'date', 'september 8 , 1986_8': 'september 8 , 1986', 'attendance_9': 'attendance', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'december 21 , 1986_12': 'december 21 , 1986', 'attendance_13': 'attendance'}

{'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'september 8 , 1986_8': [0], 'attendance_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'december 21 , 1986_12': [1], 'attendance_13': [3]}

['week', 'date', 'opponent', 'result', 'game site', 'attendance']

[['1', 'september 8 , 1986', 'new york giants', 'w 31 - 28', 'texas stadium', '59804'], ['2', 'september 14 , 1986', 'detroit lions', 'w 31 - 7', 'pontiac silverdome', '73812'], ['3', 'september 21 , 1986', 'atlanta falcons', 'l 35 - 37', 'texas stadium', '62880'], ['4', 'september 29 , 1986', 'st louis cardinals', 'w 31 - 7', 'busch memorial stadium', '49077'], ['5', 'october 5 , 1986', 'denver broncos', 'l 14 - 29', 'mile high stadium', '76082'], ['6', 'october 12 , 1986', 'washington redskins', 'w 30 - 6', 'texas stadium', '63264'], ['7', 'october 19 , 1986', 'philadelphia eagles', 'w 17 - 14', 'veterans stadium', '68572'], ['8', 'october 26 , 1986', 'st louis cardinals', 'w 37 - 6', 'texas stadium', '60756'], ['9', 'november 2 , 1986', 'new york giants', 'l 14 - 17', 'giants stadium', '74871'], ['10', 'november 9 , 1986', 'los angeles raiders', 'l 13 - 17', 'texas stadium', '61706'], ['11', 'november 16 , 1986', 'san diego chargers', 'w 24 - 21', 'jack murphy stadium', '55622'], ['12', 'november 23 , 1986', 'washington redskins', 'l 14 - 41', 'rfk stadium', '55642'], ['13', 'november 27 , 1986', 'seattle seahawks', 'l 14 - 31', 'texas stadium', '58020'], ['14', 'december 7 , 1986', 'los angeles rams', 'l 10 - 29', 'anaheim stadium', '64949'], ['15', 'december 14 , 1986', 'philadelphia eagles', 'l 21 - 23', 'texas stadium', '46117'], ['16', 'december 21 , 1986', 'chicago bears', 'l 10 - 24', 'texas stadium', '57256']]

memphis grizzlies all - time roster

https://en.wikipedia.org/wiki/Memphis_Grizzlies_all-time_roster

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16494599-13.html.csv

count

of the players on the memphis grizzlies all - time roster from the united states , two played center .

{'scope': 'subset', 'criterion': 'equal', 'value': 'center', 'result': '2', 'col': '3', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'united states'}}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nationality ; united states }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states .'}, 'position', 'center'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to center .', 'tostr': 'filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; center }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; center } }', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to center . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; center } } ; 2 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to center . the number of such rows is 2 .'}

eq { count { filter_eq { filter_eq { all_rows ; nationality ; united states } ; position ; center } } ; 2 } = true

select the rows whose nationality record fuzzily matches to united states . among these rows , select the rows whose position record fuzzily matches to center . the number of such rows is 2 .

4

4

{'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'nationality_6': 6, 'united states_7': 7, 'position_8': 8, 'center_9': 9, '2_10': 10}

{'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'nationality_6': 'nationality', 'united states_7': 'united states', 'position_8': 'position', 'center_9': 'center', '2_10': '2'}

{'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'nationality_6': [0], 'united states_7': [0], 'position_8': [1], 'center_9': [1], '2_10': [3]}

['player', 'nationality', 'position', 'years for grizzlies', 'school / club team']

[['sam mack', 'united states', 'guard - forward', '1997 - 1998', 'houston'], ['rich manning', 'united states', 'forward / center', '1995 - 1997', 'washington'], ['cuonzo martin', 'united states', 'guard - forward', '1995 - 1996', 'purdue'], ['darrick martin', 'united states', 'point guard', '1995 - 1996', 'ucla'], ['tony massenburg', 'united states', 'power forward', '1997 - 1999 , 2000 - 2002', 'maryland'], ['lee mayberry', 'united states', 'point guard', '1996 - 1999', 'arkansas'], ['oj mayo', 'united states', 'shooting guard', '2008 - 2012', 'usc'], ['darko miličić', 'serbia', 'center', '2007 - 2009', 'kk hemofarm ( serbia )'], ['mike miller', 'united states', 'small forward', '2003 - 2008', 'florida'], ['eric mobley', 'united states', 'center', '1995 - 1997', 'pittsburgh'], ['lawrence moten', 'united states', 'shooting guard', '1995 - 1997', 'syracuse'], ['eric murdock', 'united states', 'point guard', '1995 - 1996', 'providence']]

1998 icc knockout trophy

https://en.wikipedia.org/wiki/1998_ICC_KnockOut_Trophy

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11950720-1.html.csv

count

five players in the 1998 icc knockout trophy were with the new south wales first class team .

{'scope': 'all', 'criterion': 'equal', 'value': 'new south wales', 'result': '5', 'col': '5', 'subset': None}

{'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'first class team', 'new south wales'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first class team record fuzzily matches to new south wales .', 'tostr': 'filter_eq { all_rows ; first class team ; new south wales }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; first class team ; new south wales } }', 'tointer': 'select the rows whose first class team record fuzzily matches to new south wales . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; first class team ; new south wales } } ; 5 } = true', 'tointer': 'select the rows whose first class team record fuzzily matches to new south wales . the number of such rows is 5 .'}

eq { count { filter_eq { all_rows ; first class team ; new south wales } } ; 5 } = true

select the rows whose first class team record fuzzily matches to new south wales . the number of such rows is 5 .

3

3

{'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'first class team_5': 5, 'new south wales_6': 6, '5_7': 7}

{'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'first class team_5': 'first class team', 'new south wales_6': 'new south wales', '5_7': '5'}

{'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'first class team_5': [0], 'new south wales_6': [0], '5_7': [2]}

['player', 'date of birth', 'batting style', 'bowling style', 'first class team']

[['steve waugh ( captain )', '2 june 1965', 'right hand bat', 'right arm medium', 'new south wales'], ['mark waugh ( vice - captain )', '2 june 1965', 'right hand bat', 'right arm medium right arm off break', 'new south wales'], ['michael bevan', '8 may 1970', 'left hand bat', 'left arm slow chinaman', 'new south wales'], ['damien fleming', '24 april 1970', 'right hand bat', 'right arm fast - medium', 'victoria'], ['adam gilchrist ( wicket - keeper )', '14 november 1971', 'left hand bat', 'wicket - keeper', 'western australia'], ['brendon julian', '10 august 1970', 'right hand bat', 'left arm fast - medium', 'western australia'], ['michael kasprowicz', '10 february 1972', 'right hand bat', 'right arm fast - medium', 'queensland'], ['darren lehmann', '5 february 1970', 'left hand bat', 'left arm orthodox spin', 'south australia'], ['damien martyn', '21 october 1971', 'right hand bat', 'right arm medium', 'western australia'], ['glenn mcgrath', '9 february 1970', 'right hand bat', 'right arm fast - medium', 'new south wales'], ['ricky ponting', '19 december 1974', 'right hand bat', 'right arm medium', 'tasmania'], ['gavin robertson', '28 may 1966', 'right hand bat', 'right arm off break', 'new south wales'], ['andrew symonds', '9 june 1975', 'right hand bat', 'right arm medium right arm off break', 'queensland'], ['brad young', '23 february 1973', 'right hand bat', 'left arm orthodox spin', 'south australia']]

farsi1

https://en.wikipedia.org/wiki/FARSI1

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28803803-1.html.csv

ordinal

the third-highest number of episodes in a show shown on farsi1 was 135 .

{'row': '5', 'col': '5', 'order': '3', 'col_other': 'n/a', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None}

{'func': 'eq', 'args': [{'func': 'nth_max', 'args': ['all_rows', 'no of episodes', '3'], 'result': '135', 'ind': 0, 'tostr': 'nth_max { all_rows ; no of episodes ; 3 }', 'tointer': 'the 3rd maximum no of episodes record of all rows is 135 .'}, '135'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_max { all_rows ; no of episodes ; 3 } ; 135 } = true', 'tointer': 'the 3rd maximum no of episodes record of all rows is 135 .'}

eq { nth_max { all_rows ; no of episodes ; 3 } ; 135 } = true

the 3rd maximum no of episodes record of all rows is 135 .

2

2

{'eq_1': 1, 'result_2': 2, 'nth_max_0': 0, 'all_rows_3': 3, 'no of episodes_4': 4, '3_5': 5, '135_6': 6}

{'eq_1': 'eq', 'result_2': 'true', 'nth_max_0': 'nth_max', 'all_rows_3': 'all_rows', 'no of episodes_4': 'no of episodes', '3_5': '3', '135_6': '135'}

{'eq_1': [2], 'result_2': [], 'nth_max_0': [1], 'all_rows_3': [0], 'no of episodes_4': [0], '3_5': [0], '135_6': [1]}

['no', 'name', 'country', 'original channel', 'no of episodes', 'running time', 'launched', 'date', 'irst']

[['1', "lara 's choice", 'croatia', 'nova tv ( 2011 )', '182', '45 minutes', '28 jul 2012', 'saturday to wednesday', '21:00 - 22:00'], ['2', 'falling angel', 'united states', 'telemundo ( 2009 )', '182', '45 minutes', '11 mar 2013', 'saturday to wednesday', '20:00 - 21:00'], ['3', 'elisa', 'italy', 'canale 5 ( 2003 )', '68', '50 minutes', '9 feb 2013', 'saturday to wednesday', '22:00 - 23:00'], ['4', 'the queen of the south', 'united states', 'telemundo ( 2011 )', '62', '45 minutes', '1 oct 2012', 'saturday to wednesday', '12:00 - 13:00'], ['5', 'aurora', 'united states', 'telemundo ( 2010 )', '135', '45 minutes', '5 may 2012', 'saturday to wednesday', '13:00 - 14:00'], ['6', 'still standing', 'united states', 'cbs ( 2002 )', '88', '21 minutes', '9 feb 2013', 'saturday to wednesday', '17:00 - 17:30'], ['7', 'project runway', 'united states', 'bravo ( 2004 )', '58', '45 minutes', '14 feb 2013', 'thursday & friday', '20:00 - 21:00'], ['8', 'a matter of respect', 'italy', 'canale 5 ( 2006 )', '24', '50 minutes', '25 oct 2012', 'thursday & friday', '21:00 - 22:00']]

united states district court for the eastern district of california

https://en.wikipedia.org/wiki/United_States_District_Court_for_the_Eastern_District_of_California

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1065275-2.html.csv

superlative

of the judges in the united states district court for the eastern district of california , the oldest one currently living was born in 1928 .

{'scope': 'subset', 'col_superlative': '2', 'row_superlative': '9', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': 'n/a', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'present'}}

{'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'born / died', 'present'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; born / died ; present }', 'tointer': 'select the rows whose born / died record fuzzily matches to present .'}, 'born / died'], 'result': '1928 - present', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; born / died ; present } ; born / died }', 'tointer': 'select the rows whose born / died record fuzzily matches to present . the minimum born / died record of these rows is 1928 - present .'}, '1928 - present'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; born / died ; present } ; born / died } ; 1928 - present } = true', 'tointer': 'select the rows whose born / died record fuzzily matches to present . the minimum born / died record of these rows is 1928 - present .'}

eq { min { filter_eq { all_rows ; born / died ; present } ; born / died } ; 1928 - present } = true

select the rows whose born / died record fuzzily matches to present . the minimum born / died record of these rows is 1928 - present .

3

3

{'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'born / died_5': 5, 'present_6': 6, 'born / died_7': 7, '1928 - present_8': 8}

{'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'born / died_5': 'born / died', 'present_6': 'present', 'born / died_7': 'born / died', '1928 - present_8': '1928 - present'}

{'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'born / died_5': [0], 'present_6': [0], 'born / died_7': [1], '1928 - present_8': [2]}

['state', 'born / died', 'active service', 'chief judge', 'senior status', 'appointed by', 'reason for termination']

[['ca', '1914 - 2010', '1966 - 1981', '1966 - 1967', '1981 - 2010', 'eisenhower', 'death'], ['ca', '1901 - 1991', '1966 - 1969', '-', '1969 - 1991', 'eisenhower', 'death'], ['ca', '1914 - 2000', '1966 - 1979', '1967 - 1979', '1979 - 2000', 'kennedy', 'death'], ['ca', '1913 - 1998', '1969 - 1983', '1979 - 1983', '1983 - 1998', 'nixon', 'death'], ['ca', '1920 - 2005', '1979 - 1990', '-', '1990 - 2005', 'carter', 'death'], ['ca', '1919 - 1997', '1979 - 1989', '-', '1989 - 1997', 'carter', 'death'], ['ca', '1944 - present', '1980 - 1989', '-', '-', 'carter', 'resignation'], ['ca', '1930 - 2012', '1982 - 1996', '1990 - 1996', '1996 - 2006', 'reagan', 'death'], ['ca', '1928 - present', '1984 - 1996', '-', '1996 - 2012', 'reagan', 'retirement'], ['ca', '1952 - present', '1990 - 2007', '2003 - 2007', '-', 'ghw bush', 'resignation'], ['ca', '1940 - present', '1991 - 2006', '-', '2006 - 2011', 'ghw bush', 'retirement'], ['ca', '1938 - present', '1997 - 2008', '-', '2008 - 2011', 'clinton', 'retirement']]

miguel zepeda

https://en.wikipedia.org/wiki/Miguel_Zepeda

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15316394-1.html.csv

majority

in the majority of the games shown miguel zepeda scored a single goal .

{'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1', 'subset': None}

{'func': 'most_eq', 'args': ['all_rows', 'score', '1'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them are equal to 1 .', 'tostr': 'most_eq { all_rows ; score ; 1 } = true'}

most_eq { all_rows ; score ; 1 } = true

for the score records of all rows , most of them are equal to 1 .

1

1

{'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, '1_4': 4}

{'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', '1_4': '1'}

{'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], '1_4': [0]}

['goal', 'date', 'venue', 'score', 'result', 'competition']

[['1', 'july 17 , 1999', 'estadio defensores del chaco , asunción , paraguay', '2 - 1', '2 - 1', '1999 copa américa'], ['2', 'august 4 , 1999', 'estadio azteca , mexico city , mexico', '1 - 0', '4 - 3', '1999 fifa confederations cup'], ['3', 'august 4 , 1999', 'estadio azteca , mexico city , mexico', '3 - 2', '4 - 3', '1999 fifa confederations cup'], ['4', 'february 5 , 2000', 'hong kong stadium , wan chai , hong kong', '1 - 0', '1 - 0', '2000 carlsberg cup'], ['5', 'february 8 , 2000', 'hong kong stadium , wan chai , hong kong', '1 - 2', '1 - 2', '2000 carlsberg cup'], ['6', 'july 5 , 2000', 'estadio tecnológico , monterrey , mexico', '1 - 1', '2 - 1', 'friendly'], ['7', 'july 16 , 2000', 'estadio rommel fernández , panama city , panama', '1 - 0', '1 - 0', '2002 fifa world cup qualification'], ['8', 'september 3 , 2000', 'estadio azteca , mexico city , mexico', '3 - 0', '7 - 1', '2002 fifa world cup qualification']]

wzxv

https://en.wikipedia.org/wiki/WZXV

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15493221-1.html.csv

superlative

w283au has the highesst frequency of all wzxv radio station call signs .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '9', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'frequency mhz'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; frequency mhz }'}, 'call sign'], 'result': 'w283au', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; frequency mhz } ; call sign }'}, 'w283au'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w283au } = true', 'tointer': 'select the row whose frequency mhz record of all rows is maximum . the call sign record of this row is w283au .'}

eq { hop { argmax { all_rows ; frequency mhz } ; call sign } ; w283au } = true

select the row whose frequency mhz record of all rows is maximum . the call sign record of this row is w283au .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'frequency mhz_5': 5, 'call sign_6': 6, 'w283au_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'frequency mhz_5': 'frequency mhz', 'call sign_6': 'call sign', 'w283au_7': 'w283au'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'frequency mhz_5': [0], 'call sign_6': [1], 'w283au_7': [2]}

['call sign', 'frequency mhz', 'city of license', 'facility id', 'erp w', 'height m ( ft )', 'class', 'fcc info']

[['w227bw', '93.3', 'cheektowaga', '151267', '99', '-', 'd', 'fcc'], ['w248at', '97.5', 'corfy', '150935', '10', '-', 'd', 'fcc'], ['w248bc', '97.5', 'dansville', '86505', '10', '-', 'd', 'fcc'], ['w266be', '101.1', 'auburn', '138601', '27', '-', 'd', 'fcc'], ['w273af', '102.5', 'penn yan', '86524', '3', '-', 'd', 'fcc'], ['w275bl', '102.9', 'batavia', '150833', '29', '-', 'd', 'fcc'], ['w278ah', '103.5', 'syracuse / jamesville , new york', '81126', '10', '-', 'd', 'fcc'], ['w281at', '104.1', 'watkins glen', '151635', '10', '-', 'd', 'fcc'], ['w283au', '104.5', 'houghton', '151698', '10', '-', 'd', 'fcc']]

satoru nakajima

https://en.wikipedia.org/wiki/Satoru_Nakajima

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226566-2.html.csv

aggregation

the total number of points scored by satoru nakajima in his career is 19 .

{'scope': 'all', 'col': '5', 'type': 'sum', 'result': '19', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'points'], 'result': '19', 'ind': 0, 'tostr': 'sum { all_rows ; points }'}, '19'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; points } ; 19 } = true', 'tointer': 'the sum of the points record of all rows is 19 .'}

round_eq { sum { all_rows ; points } ; 19 } = true

the sum of the points record of all rows is 19 .

2

2

{'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'points_4': 4, '19_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'points_4': 'points', '19_5': '19'}

{'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'points_4': [0], '19_5': [1]}

['year', 'entrant', 'chassis', 'engine', 'points']

[['1987', 'camel team lotus honda', 'lotus 99t', 'honda v6', '7'], ['1988', 'camel team lotus honda', 'lotus 100t', 'honda v6', '1'], ['1989', 'camel team lotus', 'lotus 101', 'judd v8', '3'], ['1990', 'tyrrell racing organisation', 'tyrrell 018', 'cosworth v8', '3'], ['1990', 'tyrrell racing organisation', 'tyrrell 019', 'cosworth v8', '3'], ['1991', 'braun tyrrell honda', 'tyrrell 020', 'honda v10', '2']]

1975 world judo championships

https://en.wikipedia.org/wiki/1975_World_Judo_Championships

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15807850-2.html.csv

aggregation

the average number of bronze medals won per country at the 1975 world judo championships was 1.7 .

{'scope': 'all', 'col': '5', 'type': 'average', 'result': '1.7', 'subset': None}

{'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'bronze'], 'result': '1.7', 'ind': 0, 'tostr': 'avg { all_rows ; bronze }'}, '1.7'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; bronze } ; 1.7 } = true', 'tointer': 'the average of the bronze record of all rows is 1.7 .'}

round_eq { avg { all_rows ; bronze } ; 1.7 } = true

the average of the bronze record of all rows is 1.7 .

2

2

{'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'bronze_4': 4, '1.7_5': 5}

{'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'bronze_4': 'bronze', '1.7_5': '1.7'}

{'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'bronze_4': [0], '1.7_5': [1]}

['rank', 'nation', 'gold', 'silver', 'bronze', 'total']

[['1', 'japan', '4', '4', '3', '11'], ['2', 'soviet union', '1', '2', '3', '6'], ['3', 'france', '1', '0', '1', '2'], ['4', 'east germany', '0', '0', '2', '2'], ['5', 'italy', '0', '0', '1', '1'], ['5', 'poland', '0', '0', '1', '1'], ['5', 'north korea', '0', '0', '1', '1']]

list of whose line is it anyway ? uk episodes

https://en.wikipedia.org/wiki/List_of_Whose_Line_Is_It_Anyway%3F_UK_episodes

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14934885-1.html.csv

unique

episode 5 is the only episode of whose line is it anyway ? uk in which john bird was a performer .

{'scope': 'all', 'row': '5', 'col': '6', 'col_other': '2', 'criterion': 'equal', 'value': 'john bird', 'subset': None}

{'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'performer 4', 'john bird'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose performer 4 record fuzzily matches to john bird .', 'tostr': 'filter_eq { all_rows ; performer 4 ; john bird }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; performer 4 ; john bird } }', 'tointer': 'select the rows whose performer 4 record fuzzily matches to john bird . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'performer 4', 'john bird'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose performer 4 record fuzzily matches to john bird .', 'tostr': 'filter_eq { all_rows ; performer 4 ; john bird }'}, 'episode'], 'result': '5', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode }'}, '5'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode } ; 5 }', 'tointer': 'the episode record of this unqiue row is 5 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; performer 4 ; john bird } } ; eq { hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode } ; 5 } } = true', 'tointer': 'select the rows whose performer 4 record fuzzily matches to john bird . there is only one such row in the table . the episode record of this unqiue row is 5 .'}

and { only { filter_eq { all_rows ; performer 4 ; john bird } } ; eq { hop { filter_eq { all_rows ; performer 4 ; john bird } ; episode } ; 5 } } = true

select the rows whose performer 4 record fuzzily matches to john bird . there is only one such row in the table . the episode record of this unqiue row is 5 .

6

5

{'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'performer 4_7': 7, 'john bird_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'episode_9': 9, '5_10': 10}

{'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'performer 4_7': 'performer 4', 'john bird_8': 'john bird', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'episode_9': 'episode', '5_10': '5'}

{'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'performer 4_7': [0], 'john bird_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'episode_9': [2], '5_10': [3]}

['date', 'episode', 'performer 1', 'performer 2', 'performer 3', 'performer 4']

[['2 january 1988', '1', 'john sessions', 'stephen fry', 'dawn french', 'lenny henry'], ['9 january 1988', '2', 'john sessions', 'stephen fry', 'hugh laurie', 'enn reitel'], ['16 january 1988', '3', 'john sessions', 'stephen fry', 'nonny williams', 'jimmy mulville'], ['23 january 1988', '4', 'john sessions', 'stephen fry', 'kate robbins', 'griff rhys jones'], ['30 january 1988', '5', 'john sessions', 'stephen fry', 'jimmy mulville', 'john bird']]

list of number - one singles of 2000 ( canada )

https://en.wikipedia.org/wiki/List_of_number-one_singles_of_2000_%28Canada%29

https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17507197-1.html.csv

superlative

the artist eiffel 65 's song is the first one on the number - one singles list of 2000 .

{'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '5', 'subset': None}

{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'issue date ( s )'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; issue date ( s ) }'}, 'artist'], 'result': 'eiffel 65', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; issue date ( s ) } ; artist }'}, 'eiffel 65'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; issue date ( s ) } ; artist } ; eiffel 65 } = true', 'tointer': 'select the row whose issue date ( s ) record of all rows is minimum . the artist record of this row is eiffel 65 .'}

eq { hop { argmin { all_rows ; issue date ( s ) } ; artist } ; eiffel 65 } = true

select the row whose issue date ( s ) record of all rows is minimum . the artist record of this row is eiffel 65 .

3

3

{'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'issue date (s)_5': 5, 'artist_6': 6, 'eiffel 65_7': 7}

{'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'issue date (s)_5': 'issue date ( s )', 'artist_6': 'artist', 'eiffel 65_7': 'eiffel 65'}

{'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'issue date (s)_5': [0], 'artist_6': [1], 'eiffel 65_7': [2]}

['volume : issue', 'issue date ( s )', 'weeks on top', 'song', 'artist']

[['70:8 - 9', '13 december - 3 january 2000 ÷', '2 ÷', 'blue', 'eiffel 65'], ['70:10 - 11', '10 january - 17 january', '2', 'i knew i loved you', 'savage garden'], ['70:12', '24 january', '1', 'what a girl wants', 'christina aguilera'], ['70:13 - 14', '31 january - 7 february', '2', 'i knew i loved you', 'savage garden'], ['70:15 - 16', '14 february - 21 february', '2', 'what a girl wants', 'christina aguilera'], ['70:17 - 18', '28 february - 6 march', '2', 'show me the meaning of being lonely', 'backstreet boys'], ['70:19', '13 march', '1', 'faded', 'souldecision'], ['70:20', '20 march', '1', 'bye bye bye', "'n sync"], ['70:21 - 23', '27 march - 10 april', '3', 'never let you go', 'third eye blind'], ['70:23', '17 april', '1', 'maria maria', 'santana featuring the product g & b'], ['70:24 - 25 , 71:1 - 3', '24 april - 22 may', '5', 'it feels so good', 'sonique'], ['71:4 - 9', '29 may - 3 july', '6', 'oops ! … i did it again', 'britney spears'], ['71:10 - 12', '10 july - 24 july', '3', "it 's gon na be me", "'n sync"], ['71:13 - 14', '31 july - 7 august', '2', 'bent', 'matchbox twenty'], ['71:15', '14 august', '1', 'bang bang boom', 'the moffatts'], ['71:16 - 18', '21 august - 4 september', '3', 'bent', 'matchbox twenty'], ['71:19 - 26', '11 september - 6 november', '9', 'music', 'madonna']]