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>
of Philadelphia. Private organizations created maps designed to meet the requirements of the Federal Housing Administration's underwriting manual. The lenders had to consider FHA standards if they wanted to receive FHA insurance for their loans. FHA appraisal manuals instructed banks to steer clear of areas with "inharmonious racial groups", and recommended that municipalities enact racially restrictive zoning ordinances. Following a National Housing Conference in 1973, a group of Chicago community organizations led by The Northwest Community Organization (NCO) formed National People's Action (NPA), to broaden the fight against disinvestment and mortgage redlining in neighborhoods all over the country. This organization, led by Chicago housewife Gale Cincotta and Shel Trapp, a professional community organizer, targeted The Federal Home Loan Bank Board, the governing authority over federally chartered Savings & Loan institutions (S&L) that held at that time the bulk of the country's home mortgages. NPA embarked on an effort to build a national coalition of urban community organizations to pass a national disclosure regulation or law to require banks to reveal their lending patterns. For many years, urban community organizations had battled neighborhood decay by attacking blockbusting, forcing landlords to maintain properties, and requiring cities to board up and tear down abandoned properties. These actions addressed the short-term issues of neighborhood decline. Neighborhood leaders began to learn that these issues and conditions were symptoms of a disinvestment that was the true, though hidden, underlying cause of these problems. They changed their strategy as more data was gathered. With the help of NPA, a coalition of loosely affiliated community organizations began to form. At the Third Annual Housing Conference held in Chicago in 1974, eight hundred delegates representing 25 states and 35 cities attended. The strategy focused on the Federal Home Loan Bank Board (FHLBB), which oversaw S&Ls in cities all over the country. In 1974, Chicago's Metropolitan Area Housing Association (MAHA), made up of representatives of local organizations, succeeded in having the Illinois State Legislature pass laws mandating disclosure and outlawing redlining. In Massachusetts, organizers allied with NPA confronted a unique situation. Over 90% of home mortgages were held by state-chartered savings banks. A Jamaica Plain neighborhood organization pushed the disinvestment issue into the statewide gubernatorial race. The Jamaica Plain Banking & Mortgage Committee and its citywide affiliate, The Boston Anti-redlining Coalition (BARC), won a commitment from Democratic candidate Michael S. Dukakis to order statewide disclosure through the Massachusetts State Banking Commission. After Dukakis was elected, his new Banking Commissioner ordered banks to disclose mortgage-lending patterns by ZIP code. The suspected redlining was revealed.A former community organizer, Richard W. "Rick" Wise who led the Boston organizing, has published a novel, Redlined, which gives a somewhat fictionalized account of the anti-redlining campaign. NPA and its affiliates achieved disclosure of lending practices with the passage of The Home Mortgage Disclosure Act of 1975. The required transparency and review of loan practices began to change lending practices. NPA began to work on reinvestment in areas that had been neglected. Their support helped gain passage in 1977 of the Community Reinvestment Act. Challenges Court system The U.S. Department of Housing and Urban Development announced a $200 million settlement with Associated Bank over redlining in Chicago and Milwaukee in May 2015. The three-year HUD observation led to the complaint that the bank purposely rejected mortgage applications from black and Latino applicants. The final settlement required AB to open branches in non-white neighborhoods, just like HSBC. New York Attorney General Eric Schneiderman announced a settlement with Evans Bank for $825,000 on September 10, 2015. An investigation had uncovered the erasure of black neighborhoods from mortgage lending maps.
</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.