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>
instruments became a major research program in the 90s. Examples of this line of research are well-known papers by Arellano-Bond (1991), Ahn-Schmidt (1995), Arellano-Bover (1995) and Blundell-Bond (1998). For a review of the literature see Baltagi (2005) chapter 8. More recent work, such as Ziliak (1997), supports Keane and Runkle (1992)'s original argument that use of additional instruments may cause severe bias. Econometric methodology Keane is well known as a champion of the "structural econometrics" school, which emphasizes the important role of economic theory in empirical work. This contrasts with the "experimental school" which has become very popular in the last 20 years. The latter seeks to use "natural experiments" to substitute for economic theory. He has written a number of articles on the importance of theory and the limitations of experiments (see Keane 2010a, 2010b). Other areas In addition, Keane has done significant work in many other areas, such as health economics, child development, international trade, political economy, experimental economics, and development economics. Notable students Tulin Erdem (Ph.D. 1993), who is the Leonard Stern Professor of Business at New York University, was editor-in-chief of the Journal of Marketing Research, one of the core journals in marketing, from 2009–2012. She was also the president of the Institute for Operations Research and the Management Sciences (INFORMS) Marketing Society (ISMS) from 2006–2007. Dan Houser (Ph.D. 1998) is the director of the Interdisciplinary Center for Economic Science and the chair of the Department of Economics at George Mason University. Raquel Bernal (Ph.D. 2003) is the director of the Center for the Study of Economic Development (CEDE) at the Universidad de los Andes in Bogotá, Colombia, and the 2013 recipient of the Juan Luis Londoño Prize for an economist under 40 whose research has contributed significantly to the well-being of Colombians. Keane has advised several other students who have also made significant contributions in various areas of economics and business, including Susumu Imai (labor supply, Bayesian methods), Andrew Ching (Marketing, Empirical IO), Susan Feinberg (trade), Katherine Harris (health) and Ahmed Khwaja (Marketing, Empirical IO, health). Selected papers Testing the rationality of price forecasts: New evidence from panel data, (with David Runkle), American Economic Review, 80:4, (1990), 714–35. On the Estimation of Panel Data Models with Serial Correlation when Instruments are Predetermined but not Strictly Exogenous, (with David Runkle), Journal of Business and Economic Statistics, 10:1, (1992), 1–9. Simulation Estimation for Panel Data Models with Limited Dependent Variables, in The Handbook of Statistics, G.S. Maddala, C. R. Rao and H.D. Vinod editors, North Holland publisher (1993). A Computationally Practical Simulation Estimator for Panel Data, Econometrica, 62:1, (1994), 95–116. The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation: Monte Carlo Evidence, (with Kenneth Wolpin), Review of Economics and Statistics, 76:4, (1994), 648–72. Decision Making under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets (with Tulin Erdem), Marketing Science, 15:1, (1996), 1–20. The Career Decisions of Young Men, (with Kenneth Wolpin), Journal of Political Economy, 105:3, (1997), 473–522. A Structural Model of Multiple Welfare Program Participation and Labor Supply, (with Robert A. Moffitt), International Economic Review, 39:3, (1998), 553–89. Are Financial Analysts' Forecasts of Corporate Profits Rational? (with David Runkle), Journal of Political Economy, 106:4, (1998), 768–805. A Model of Health Plan Choice: Inferring Preferences and Perceptions from a Combination of Revealed Preference and Attitudinal Data, (with Katherine Harris), Journal of Econometrics, 89, (1999), 131–57. Mixture of Normals Probit Models, (with John Geweke), in Analysis of Panels and Limited Dependent Variable Models, Hsiao, Lahiri, Lee and Pesaran editors, Cambridge University Press, (1999), 49–78. Bayesian Inference for Dynamic Discrete Choice Models without
</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.