You are an AI assistant tasked with analyzing a passage of text to insert retrieval tokens and identify Prefix-intent (PI) and Key Factual Information (KFI). A Prefix-Intent (PI) should be a piece of text that should precede factual information, it must not be only 1 or 2 words. Key Factual Information (KFI) should be meaningful and include important details.

Your goal is to insert a  <|retrieval|> token after it is clear what the intent of the text is (e.g., "The president of the USA is"), but  before the key factual information is stated (e.g., "Joe Biden"). Whenever there is a <|retrieval|> token, we will retrieve the key factual information.

Here is the passage you will be working with:

<passage>
1996 E-series was introduced and Sisu's got again new model codes. The number following the letter E was now for the engine displacement in litres. After this was letter M followed by the engine output in horsepower. The M was substituted by gap when the Renault cabin was introduced in 1997. Confusingly, the model was written differently on the badge; for example on E18 630 the model is written as 18 E 630. The 2005 introduced last model with Renault cabins got extra trade names although the official model code was still with E. C500 was used with models powered by Caterpillar and R500 with Renault engine. Also application specific trade names were taken to use: logging vehicle became Sisu Timber, earthmover Sisu Rock, hook loader Sisu Roll, road maintenance vehicle was named Sisu Works, mobile crane Sisu Crane respectively and wheeled vehicle transporter Sisu Carrier. The 2010 introduced model with Mercedes-Benz components got the trade name Polar. The early model names are DK12M and DK16M; the K stands for high cabin, the number for engine displacement in litres and M means that the cabin is forward control type. The application specific model names are still used in parallel. In 2014 the company introduced new models, model names CK and CM while the commercial name for these products continues as Polar. Models The currently produced models are bolded. Lorries S-321 1932 S-322 1932 S-341 1934 SO-2 1934 SO-3 1934 SO-1K 1934 SH-1 1935 SH-2 1935 SH-3 1935 SH-4 1935 SH-6 1936 SH-9 1938 SH-12 1938 SH-15 1939 SB-18 1939 SB-20 1939 SB-19 1940 S-21 1943 S-22 1944 K-23 1949 S-25 K-25 1951 K-28 1951 B-56 1951 KB-27 1952 K-29 1952 K-30 1953 K-31 1953 K-33 1953 SH-1/53 1953 KB-24 1955 K-26 1955 K-32 1955 K-34 1955 K-36 1955 K-38 1955 K-41 1957 K-43 1957 K-37 1958 K-39 1958 K-40 1959 K-44 1959 KB-47 1959 KB-48 1959 K-35 1960 K-108 1960 K-50SS 1961 KB-107 1961 KB-124 1961 KB-102 1962 KB-112 1962 K-138 1963 K-134 1963 K-143 1963 K-42 1964 KB-45 1964 KB-117 1964 K-137 1964 K-145 1965 K-148 1965 K-142 1966 K-149 1966 K-124 1967 K-131 1967 U-131 1967 K-132 1967 K-136 U-132 1967 U-138 1967 U-139 1967 K-141 1967 K-144 1967 KB-46 1968 KB-121 1968 U-135 1968 U-137 1968 UM-138 1968 UP-138 1968 L-139 1968 L-132 1969 LP-138 1969 LM-138 1969 MS-162 1969 A-45 1970 AH-45 1970 L-131 1970 LV-132 1970 L-137 1970 R-141 1970 R-142 1970 R-148 1970 R-149 1970 M-162 1970 R-145 1971 M-161 1971 R-143 1972 LV-131 1973 LV-139 M-163 1973 SB-171 SN 171 R-144 1976 AS-45 1977 SB-127 1977 R-146 1978 MA-162 1978 M-168 1978 L-135 1979 SB 140 1979 MI-161 1979 MI-162 1979 MK-162 1979 RR-143 1980 SB 150 1980 SK 150 1980 SK 170 SA-150 1982 SC 150 1982 SD 150 SR 220 1982 SR 280 1982 SM 220 1983 SM 280 1983 SA-180 1983 SR 312 SRH 300 SRH 450 SL 170 1983 SL 171 SM 242 SL 190 1983 SL 210 1983 SA-240 1984 SK 190 1984 SK 210 1984 SM 260 1984 SM 300 1984 SM 320 1984 SR 260 1984 SR 300 1984 SR 320 1984 SM 332 SN 220 SN 260 SA-110 1986 SK 171 SA-170 1987 SK 250 1988 SL 250 1988 SM 340 1988 SR 340 1988 SA-130 1989 SA-241 1990 SK 181 1990 SL 181 1990 SM 270 1990 SR 270 1990 SA-151 1991 SA-210 1992 SK 242 SK 192 1993 SK 262 1993 SM 282 1993 SM 372 1993 SR 282 1993 SR 372 1993 SK 283 1995 SM 283 1995 SM 313 1995 SM
</passage>

Follow these steps to complete the task:

1. Inserting retrieval tokens:
   a. Read through the text carefully.
   b. Identify potential Prefix-Intent (PI) sections that consist of a single intent and could serve as a search query. You must not modify the prefix intent and it MUST be part verbatim in the passage. It cannot be single or only a few words.
   c. For each identified PI, evaluate if the following sentence/subsentece that could serve as Key Factual Information (KFI).
   d. Insert <|retrieval|> tokens into the text AFTER the Prefix Intent (PI) and BEFORE key factual information is stated (KFI).
   e. Key Factual information should not intersect with Prefix Intent.

2. Identifying Prefix-Intent (PI) and Key Factual Information (KFI):
   a. For each <|retrieval|> token you've inserted, identify the preceding text as the Prefix-Intent (PI).
   b. Identify the following text, up to the next PI, as the Key Factual Information (KFI).
   c. Ensure that both PI and FI are short subsentences or phrases but not 1 or 2 words.

3. Insert <|start-PI|>,<|end-PI|>, <|start-KFI|>, <|end-KFI|>.
   a. For each identified Prefix Intent (PI), add a start and end special tokens (<|start-PI|>,<|end-PI|>) to quickly identify them.
   b. Do the same for the Key Factual Information (KFI) (<|start-KFI|>, <|end-KFI|>)
   c. Ensure that the very first PI in the passage is preceded by <|start-PI|>.
   d. Double-check that every PI and KFI is properly enclosed in these tags.

4. Describing ideal retrieved information:
   a. Given the Prefix-Intent (PI) and Key Factual Information (KFI), imagine an ideal retriever and enormous corpus. 
   b. Using the retriever and corpus, imagine that you can retrieve information given the Prefix-Intent.  
   c. Describe how that content should look like so it helps generating the Key Factual Information (KFI)


5. Reformulate the Prefix Intent if necessary: Reformulate it to not be impersonal. This means that if the subject it is not identified (e.g. it, he, she) you need to add the appropriate subject so the prefix-intent can be understood by itself. The subject should be changed from the previous information in the passage. Reformulated Prefix should not add any extra information or intent that is not mentioned in the original Prefix Intent.


6. Output format:
   For each pair of Prefix-Intent (PI) and Key Factual Information (KFI) you identify, provide the following output separating each example in a JSON list output. Ensure that it is actually a JSON.

   - Prefix_intent: [Write the Prefix Intent here]
   - Prefix_reformulated: [Write the Prefix Intent reformulated]
   - Key_Factual_Information: [Write the Key Factual Information here]
   - Verification: [Confirm that the KFI can be directly derived from the PI given a strictly connected context]
   - Ideal_Knowledge: [Describe how the ideal retrieved content should look to generate the KFI]
   - Explanation: [Briefly explain how the PI and KFI connect and why you chose to insert the <|retrieval|> token in that position]


7. Final step:
   After you have completed the analysis and provided outputs for all identified PI-KFI pairs, present the original passage with the <|retrieval|> tokens inserted in their appropriate positions

Here's an example of the expected output format:

Passage: Girmal Falls General This waterfall extends to a height of up to 100 feet, making it the highest waterfall of Gujarat. The picturesque beauty of this site makes it popular among visitors and people of the region alike. The water swiftly falls from a great height, creating a fog like condition that’s eye catching. The government of this state is working on many projects to make this place an ideal picnic spot and a tourist attraction. The fall comes to its best form at the time of monsoon and provides an immensely striking appearance. Some of the best natural features of Gujarat can be explored in this place. This place is a nice and refreshing retreat for any traveler.

```json
{ "results" : [
  {
    "Prefix_intent": "Girmal Falls General This waterfall extends to a height of up to",
    "Prefix_reformulated": "What is the height of Girmal Falls?",
    "Key_Factual_Information": "100 feet, making it the highest waterfall of Gujarat",
    "Verification": "The KFI directly provides the height information requested in the PI.",
    "Ideal_Knowledge": "A list of waterfalls in Gujarat, India, with their heights and rankings, specifically mentioning Girmal Falls as the highest at 100 feet.",
    "Explanation": "The PI introduces Girmal Falls and sets up a query about its height, while the KFI provides the specific measurement and its significance. The <|retrieval|> token is inserted to indicate where additional information might be needed to support the factual statement."
  },
  {
    "Prefix_intent": "The water swiftly falls from a great height, creating",
    "Prefix_reformulated": "What does the water falling from Girmal Falls create?",
    "Key_Factual_Information": "a fog like condition that's eye catching",
    "Verification": "The KFI directly describes the effect created by the falling water, as prompted by the PI.",
    "Ideal_Knowledge": "Detailed descriptions of the visual effects created by high waterfalls, including the formation of mist or fog-like conditions due to water droplets in the air.",
    "Explanation": "The PI describes the water falling and sets up a query about its effect, while the KFI provides the specific visual phenomenon created. The <|retrieval|> token is inserted to indicate where additional information might be needed to support the description."
  },
  {
    "Prefix_intent": "The fall comes to its best form at the time of",
    "Prefix_reformulated": "When does Girmal Falls come to its best form?",
    "Key_Factual_Information": "monsoon and provides an immensely striking appearance",
    "Verification": "The KFI directly answers when the waterfall is at its best and describes its appearance, as prompted by the PI.",
    "Ideal_Knowledge": "Information on the seasonal variations of Girmal Falls, highlighting the monsoon season's impact on water flow and visual appeal.",
    "Explanation": "The PI sets up a query about the best time to view the falls, and the KFI provides the specific season and describes the appearance. The <|retrieval|> token is inserted to indicate where additional information about seasonal changes might be needed."
  }],
"Updated_Passage":"<|start-PI|> Girmal Falls General This waterfall extends to a height of up to <|end-PI|> <|retrieval|> <|start-KFI|> 100 feet, making it the highest waterfall of Gujarat <|end-KFI|>. The picturesque beauty of this site makes it popular among visitors and people of the region alike. <|start-PI|> The water swiftly falls from a great height, creating <|end-PI|> <|retrieval|> <|start-KFI|> a fog like condition that’s eye catching <|end-KFI|>. The government of this state is working on many projects to make this place an ideal picnic spot and a tourist attraction. <|start-PI|> The fall comes to its best form at the time of <|end-PI|> <|retrieval|> <|start-KFI|> monsoon and provides an immensely striking appearance <|end-KFI|>. Some of the best natural features of Gujarat can be explored in this place. This place is a nice and refreshing retreat for any traveler."
}

```
8. Final check:
  a. Review the Updated_Passage to ensure all PIs and KFIs are properly tagged.
  b. Verify that the first PI in the passage starts with <|start-PI|>.
  c. Make sure there are no missing or misplaced tags.
 
Remember to analyze the entire passage, identify all relevant PI-KFI pairs, and provide the output for each pair as specified. After completing the analysis, present the original passage with the <|retrieval|> tokens inserted in their appropriate positions as part of a new key in the JSON file. Only Ouput the JSON File.