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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Insel Hiddensee", "col1": 1000.0}, {"col0": "Golden Sands", "col1": null}, {"col0": "Katwijk aan Zee", "col1": null}, {"col0": "Nieblum", "col1": 569.0}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Insel Hiddensee","col1":1000.0},{"col0":"Golden Sands","col1":null},{"col0":"Katwijk aan Zee","col1":null},{"col0":"Nieblum","col1":569.0}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "nickel", "col1": 1.0}, {"col0": "DDT", "col1": 1.0}, {"col0": "4-Aminobiphenyl", "col1": 0.1}, {"col0": "tellurium", "col1": 0.1}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"nickel","col1":1.0},{"col0":"DDT","col1":1.0},{"col0":"4-Aminobiphenyl","col1":0.1},{"col0":"tellurium","col1":0.1}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "2016 Tour de Normandie, Prologue", "col1": 6.4}, {"col0": "2004 Tour de France, Prologue", "col1": 6.1}, {"col0": "Crit\u00e9rium du Dauphin\u00e9 2011, prologue", "col1": 5.4}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"2016 Tour de Normandie, Prologue","col1":6.4},{"col0":"2004 Tour de France, Prologue","col1":6.1},{"col0":"Critérium du Dauphiné 2011, prologue","col1":5.4}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Unnamed: 0": 0, "col0": "TableTop", "col1": 0.0, "col2": 0.73, "col3": -0.05, "col4": "().74", "col5": 8.7e-08, "col6": 0.6000001}, {"Unnamed: 0": 1, "col0": "StartPoint", "col1": 0.2035806, "col2": 0.78, "col3": -0.25, "col4": 0.05000001, "col5": 0.04999995, "col6": 0.05000001}, {"Unnamed: 0": 2, "col0": "EndPoint", "col1": 0.0, "col2": "().7&", "col3": -0.315, "col4": 0.075, "col5": 0.07500005, "col6": "().()74qqqqq"}, {"Unnamed: 0": 3, "col0": "ShelfBoundary", "col1": 0.0, "col2": 0.87, "col3": -0.05, "col4": 0.74, "col5": "().()|qqqqq&", "col6": 0.6}, {"Unnamed: 0": 4, "col0": "LeftBoundary", "col1": -0.36, "col2": 0.795, "col3": -0.05, "col4": 0.01999998, "col5": 0.13, "col6": 0.6}, {"Unnamed: 0": 5, "col0": "RightBoundary", "col1": 0.36, "col2": 0.795, "col3": -0.05, "col4": 0.01999998, "col5": 0.13, "col6": 0.6}, {"Unnamed: 0": 6, "col0": "BackBoundary", "col1": 0.0, "col2": 0.795, "col3": 0.24, "col4": "().74", "col5": 0.13, "col6": 0.02000001}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Unnamed: 0":0,"col0":"TableTop","col1":0.0,"col2":0.73,"col3":-0.05,"col4":0.74,"col5":0.000000087,"col6":0.6000001},{"Unnamed: 0":1,"col0":"StartPoint","col1":0.2035806,"col2":0.78,"col3":-0.25,"col4":0.05000001,"col5":0.04999995,"col6":0.05000001},{"Unnamed: 0":2,"col0":"EndPoint","col1":0.0,"col2":0.78,"col3":-0.315,"col4":0.075,"col5":0.07500005,"col6":0.07499999},{"Unnamed: 0":3,"col0":"ShelfBoundary","col1":0.0,"col2":0.87,"col3":-0.05,"col4":0.74,"col5":0.01999998,"col6":0.6},{"Unnamed: 0":4,"col0":"LeftBoundary","col1":-0.36,"col2":0.795,"col3":-0.05,"col4":0.01999998,"col5":0.13,"col6":0.6},{"Unnamed: 0":5,"col0":"RightBoundary","col1":0.36,"col2":0.795,"col3":-0.05,"col4":0.01999998,"col5":0.13,"col6":0.6},{"Unnamed: 0":6,"col0":"BackBoundary","col1":0.0,"col2":0.795,"col3":0.24,"col4":0.74,"col5":0.13,"col6":0.02000001}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "IYBC0", "col1": "baked milk", "col2": "Russia, Belarus, Ukraine, Belarus, Ukraine, Belarus, Ukraine, Belarus, Ukraine, Belarus, Ukraine", "col3": "food produced from or containing the milk of mammals"}, {"col0": "IYBC1", "col1": "suzma, cow's milk, cow's milk, cow's milk, cow's milk, cow's milk", "col2": "Turkey, Afghanistan, Afghanistan, Afghanistan, Afghanistan", "col3": "?"}, {"col0": "IYBC2", "col1": "sheep milk, cow's milk, cow's milk, cow's milk, cow's milk, cow's milk, cow's milk", "col2": "Turkey", "col3": "?"}, {"col0": "IYBC3", "col1": "milk, sugar, sugar, sugar, sugar", "col2": "Netherlands", "col3": "?"}, {"col0": "IYBC4", "col1": "double cream", "col2": "United Ringdom", "col3": "food produced from or containing the milk of mammals"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"IYBC0","col1":"baked milk","col2":"Russia, Belarus, Ukraine, Belarus, Ukraine, Belarus, Ukraine, Belarus, Ukraine, Belarus, Ukraine","col3":"food produced from or containing the milk of mammals"},{"col0":"IYBC1","col1":"suzma, cow's milk, cow's milk, cow's milk, cow's milk, cow's milk","col2":"Turkey, Afghanistan, Afghanistan, Afghanistan, Afghanistan","col3":"?"},{"col0":"IYBC2","col1":"sheep milk, cow's milk, cow's milk, cow's milk, cow's milk, cow's milk, cow's milk","col2":"Turkey","col3":"?"},{"col0":"IYBC3","col1":"milk, sugar, sugar, sugar, sugar","col2":"Netherlands","col3":"?"},{"col0":"IYBC4","col1":"double cream","col2":"United Kingdom","col3":"food produced from or containing the milk of mammals"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "edible plant or part of a plant, involved in cooking": "8DS6G120HJ"}, {"Prop0": "Prop2", "edible plant or part of a plant, involved in cooking": "10006124"}, {"Prop0": "Prop3", "edible plant or part of a plant, involved in cooking": "horseradish plant"}, {"Prop0": "Prop4", "edible plant or part of a plant, involved in cooking": "Russian cuisine, German cuisine, Czech cuisine, Ukrainian cuisine, Belarusian cuisine"}, {"Prop0": "Prop5", "edible plant or part of a plant, involved in cooking": "4625"}, {"Prop0": "PropG", "edible plant or part of a plant, involved in cooking": "Roots of armoracia rusticana"}, {"Prop0": "Prop7", "edible plant or part of a plant, involved in cooking": "odor, pungency"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","edible plant or part of a plant, involved in cooking":"8DS6G120HJ"},{"Prop0":"Prop2","edible plant or part of a plant, involved in cooking":"10006124"},{"Prop0":"Prop3","edible plant or part of a plant, involved in cooking":"horseradish plant"},{"Prop0":"Prop4","edible plant or part of a plant, involved in cooking":"Russian cuisine, German cuisine, Czech cuisine, Ukrainian cuisine, Belarusian cuisine"},{"Prop0":"Prop5","edible plant or part of a plant, involved in cooking":"4625"},{"Prop0":"Prop6","edible plant or part of a plant, involved in cooking":"Roots of armoracia rusticana"},{"Prop0":"Prop7","edible plant or part of a plant, involved in cooking":"odor, pungency"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "ALJW0", "col1": "type of cheese"}, {"col0": "ALJW1", "col1": "Italian cheese"}, {"col0": "ALJW2", "col1": "Italian cheese"}, {"col0": "ALJW3", "col1": "Italian cheese"}, {"col0": "ALJW4", "col1": "1ta1ian cheese"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"ALJW0","col1":"type of cheese"},{"col0":"ALJW1","col1":"Italian cheese"},{"col0":"ALJW2","col1":"Italian cheese"},{"col0":"ALJW3","col1":"Italian cheese"},{"col0":"ALJW4","col1":"Italian cheese"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Crazy Cow Creek", "col1": 2.8, "col2": 1092}, {"col0": "Bear Haven Creek", "col1": 5.25, "col2": 108}, {"col0": "Wiest Creek", "col1": 8.1, "col2": 112}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Crazy Cow Creek","col1":2.8,"col2":1092},{"col0":"Bear Haven Creek","col1":5.25,"col2":108},{"col0":"Wiest Creek","col1":8.1,"col2":112}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "WOME0", "col1": "the concept going for a Wa1k Whi1e eating during your 1unchbreak"}, {"col0": "WOME1", "col1": "a lunch carried to a destination"}, {"col0": "WOME2", "col1": "providing of a meal at no cost, usually as a sales enticement to attract customers"}, {"col0": "WOME3", "col1": "business lunch"}, {"col0": "WOME4", "col1": "mix between breakfast and lunch, generally in the late morning"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"WOME0","col1":"the concept going for a walk while eating during your lunchbreak"},{"col0":"WOME1","col1":"a lunch carried to a destination"},{"col0":"WOME2","col1":"providing of a meal at no cost, usually as a sales enticement to attract customers"},{"col0":"WOME3","col1":"business lunch"},{"col0":"WOME4","col1":"mix between breakfast and lunch, generally in the late morning"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Met Museum artworks on display"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Metropolitan Museum of Art, European Sculpture and Decorative Arts"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Met Museum artworks on display"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Metropolitan Museum of Art, European Sculpture and Decorative Arts"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "NGC 4410", "col1": null}, {"col0": "IC 254", "col1": 40.5262125}, {"col0": "UGC 22", "col1": 1.0211417}, {"col0": "NGC 707", "col1": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"NGC 4410","col1":null},{"col0":"IC 254","col1":40.5262125},{"col0":"UGC 22","col1":1.0211417},{"col0":"NGC 707","col1":null}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "T4", "col1": 60, "col2": null}, {"col0": "IR4", "col1": 180, "col2": null}, {"col0": "Lu*torpeda", "col1": 115, "col2": 6.0}, {"col0": "BOA", "col1": 72, "col2": null}, {"col0": "DB Class 732", "col1": 45, "col2": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"T4","col1":60,"col2":null},{"col0":"IR4","col1":180,"col2":null},{"col0":"Luxtorpeda","col1":115,"col2":6.0},{"col0":"BOA","col1":72,"col2":null},{"col0":"DB Class 732","col1":45,"col2":null}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Unnamed: 0": 0, "col0": "e73a74ac-8fd1-11eb-924d-9cd76263cbd0", "col1": "genus", "col2": "Neostrearia", "col3": null, "col4": null, "col5": "L.S.Sm.", "col6": "Q2884481", "col7": 1958, "col8": null, "col9": "Proc. Roy. Soc. Queensland 69: 46. 1958.", "col10": null, "col11": null, "col12": null, "col13": null, "col14": null, "col15": "19607-1", "col16": "wfo-4000025854", "col17": 7329043, "col18": null, "col19": null}, {"Unnamed: 0": 1, "col0": "3792f33a-8fcc-11eb-924d-9cd76263cbd0", "col1": "species", "col2": "f1eckeri", "col3": "Neostrearia", "col4": null, "col5": "L.S.Sm.", "col6": "Q2884481", "col7": 1958, "col8": null, "col9": "Proc. Roy. Soc. Queensland 69: 46 1958", "col10": null, "col11": null, "col12": null, "col13": null, "col14": null, "col15": "43()737~|", "col16": "wfo-0000379467", "col17": 3702502, "col18": null, "col19": null}, {"Unnamed: 0": "Z", "col0": "0177949e-8fcd-11eb-924d-9cd76263cbd0", "col1": "species", "col2": "eucrosioides", "col3": "Neostricklandia", "col4": null, "col5": "(Baker) Rauschert", "col6": "Q73()33(),Q33ZGq3q", "col7": 1982, "col8": null, "col9": "Taxon 31: 555 1982", "col10": null, "col11": "Q28955334", "col12": null, "col13": "f6ebedd6-8fcc-11eb-924d-9cd76263cbd0", "col14": null, "col15": "910974-1", "col16": "wfo-0000701830", "col17": 5326980, "col18": null, "col19": null}, {"Unnamed: 0": 3, "col0": "bb17d20c-8fd1-11eb-924d-9cd76263cbd0", "col1": "species", "col2": "integrifolia", "col3": "Neostyphonia", "col4": null, "col5": "(Nutt.) Shafer", "col6": "Q728337,Q615605", "col7": 1908, "col8": null, "col9": "N. Amer. Trees 612 1908", "col10": null, "col11": null, "col12": null, "col13": null, "col14": null, "col15": "169104-2", "col16": "wfo-0001244978", "col17": 3660601, "col18": null, "col19": null}, {"Unnamed: 0": 4, "col0": "bb17f7be-8fd1-11eb-924d-9cd76263cbd0", "col1": "species", "col2": "ovata", "col3": "Neostyphonia", "col4": null, "col5": "(S.Watson) Abrams", "col6": "Q960820,Q2698829", "col7": 1910, "col8": null, "col9": "Bull. New York Bot. Gard. 6(21): 403 1910", "col10": null, "col11": null, "col12": null, "col13": null, "col14": null, "col15": "169105-2", "col16": "wfo-0001244979", "col17": 3660582, "col18": null, "col19": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Unnamed: 0":0,"col0":"e73a74ac-8fd1-11eb-924d-9cd76263cbd0","col1":"genus","col2":"Neostrearia","col3":null,"col4":null,"col5":"L.S.Sm.","col6":"Q2884481","col7":1958,"col8":null,"col9":"Proc. Roy. Soc. Queensland 69: 46. 1958.","col10":null,"col11":null,"col12":null,"col13":null,"col14":null,"col15":"19607-1","col16":"wfo-4000025854","col17":7329043,"col18":null,"col19":null},{"Unnamed: 0":1,"col0":"3792f33a-8fcc-11eb-924d-9cd76263cbd0","col1":"species","col2":"fleckeri","col3":"Neostrearia","col4":null,"col5":"L.S.Sm.","col6":"Q2884481","col7":1958,"col8":null,"col9":"Proc. Roy. Soc. Queensland 69: 46 1958","col10":null,"col11":null,"col12":null,"col13":null,"col14":null,"col15":"430737-1","col16":"wfo-0000379467","col17":3702502,"col18":null,"col19":null},{"Unnamed: 0":2,"col0":"0177949e-8fcd-11eb-924d-9cd76263cbd0","col1":"species","col2":"eucrosioides","col3":"Neostricklandia","col4":null,"col5":"(Baker) Rauschert","col6":"Q730330,Q3326939","col7":1982,"col8":null,"col9":"Taxon 31: 555 1982","col10":null,"col11":"Q28955334","col12":null,"col13":"f6ebedd6-8fcc-11eb-924d-9cd76263cbd0","col14":null,"col15":"910974-1","col16":"wfo-0000701830","col17":5326980,"col18":null,"col19":null},{"Unnamed: 0":3,"col0":"bb17d20c-8fd1-11eb-924d-9cd76263cbd0","col1":"species","col2":"integrifolia","col3":"Neostyphonia","col4":null,"col5":"(Nutt.) Shafer","col6":"Q728337,Q615605","col7":1908,"col8":null,"col9":"N. Amer. Trees 612 1908","col10":null,"col11":null,"col12":null,"col13":null,"col14":null,"col15":"169104-2","col16":"wfo-0001244978","col17":3660601,"col18":null,"col19":null},{"Unnamed: 0":4,"col0":"bb17f7be-8fd1-11eb-924d-9cd76263cbd0","col1":"species","col2":"ovata","col3":"Neostyphonia","col4":null,"col5":"(S.Watson) Abrams","col6":"Q960820,Q2698829","col7":1910,"col8":null,"col9":"Bull. New York Bot. Gard. 6(21): 403 1910","col10":null,"col11":null,"col12":null,"col13":null,"col14":null,"col15":"169105-2","col16":"wfo-0001244979","col17":3660582,"col18":null,"col19":null}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
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"2017-07-30T12:03:00", "monthyear": "2017-07-30T12:03:00", "date_of_birth": "2017-06-07", "outcome_type": "Adoption", "outcome_subtype": null, "animal_type": "Cat", "sex_upon_outcome": "Intact Male", "age_upon_outcome": "1 month", "breed": "Domestic Shorthair Mix", "color": "Brown Tabby/White"}, {"animal_id": "A683115", "name": null, "datetime": "2014-07-07T14:04:00", "monthyear": "2014-07-07T14:04:00", "date_of_birth": "2014-01-07", "outcome_type": "Euthanasia", "outcome_subtype": "Kabies Kisk", "animal_type": "Other", "sex_upon_outcome": "Unknown", "age_upon_outcome": "S months", "breed": "Bat Mix", "color": "Brown"}, {"animal_id": "A664462", "name": "*Edgar", "datetime": "2013-10-07T13:06:00", "monthyear": "2013-10-07T13:06:00", "date_of_birth": "2013-06-03", "outcome_type": "Transfer", "outcome_subtype": "Partner", "animal_type": "Dog", "sex_upon_outcome": "Intact Male", "age_upon_outcome": "4 months", "breed": "Leonberger Mix", "color": "Brown/White"}, {"animal_id": "A785671", "name": "Toots", "datetime": "2018-12-07T17:36:00", "monthyear": "2018-12-07T17:36:00", "date_of_birth": "2015-12-07", "outcome_type": "Return to Owner", "outcome_subtype": null, "animal_type": "Cat", "sex_upon_outcome": "Intact Female", "age_upon_outcome": "3 years", "breed": "Domestic Shorthair Mix", "color": "Blue Tabby"}, {"animal_id": "A762837", "name": null, "datetime": "2017-12-02T16:24:00", "monthyear": "2017-12-02T16:24:00", "date_of_birth": "2016-11-28", "outcome_type": "Transfer", "outcome_subtype": "Partner", "animal_type": "Dog", "sex_upon_outcome": "Neutered Male", "age_upon_outcome": "1 year", "breed": "Australian Shepherd/Nova Scotia Duck Tolling Retriever", "color": "Chocolate/White"}, {"animal_id": "A713785", "name": null, "datetime": "2015-10-19T14:55:00", "monthyear": "2015-10-19T14:55:00", "date_of_birth": "2015-05-29", "outcome_type": "Transfer", "outcome_subtype": "Partner", "animal_type": "Dog", "sex_upon_outcome": "Neutered Ma1e", "age_upon_outcome": "4 months", "breed": "Labrador Retriever Mix", "color": "Red"}, {"animal_id": "A74|7&3", "name": null, "datetime": "2017-01-12T17:02:00", "monthyear": "2017-01-12T17:02:00", "date_of_birth": "Z()|G~||~|&", "outcome_type": "Adoption", "outcome_subtype": null, "animal_type": "Dog", "sex_upon_outcome": "Spayed Female", "age_upon_outcome": "1 month", "breed": "Labrador Retriever Mix", "color": "Tan/White"}, {"animal_id": "A764447", "name": null, "datetime": "2018-01-18T16:59:00", "monthyear": "2018-01-18T16:59:00", "date_of_birth": "Z()|7~|()~3()", "outcome_type": "Adoption", "outcome_subtype": null, "animal_type": "Dog", "sex_upon_outcome": "Spayed Female", "age_upon_outcome": "2 months", "breed": "Pit Bull Mix", "color": "Brown/White"}, {"animal_id": "A697000", "name": "Boone", "datetime": "2015-02-16T13:24:00", "monthyear": "2015-02-16T13:24:00", "date_of_birth": "2009-02-16", "outcome_type": "Return to Owner", "outcome_subtype": null, "animal_type": "Dog", "sex_upon_outcome": "Neutered Male", "age_upon_outcome": "6 years", "breed": "English Springer Spaniel Mix", "color": "Liver/White"}, {"animal_id": "A777321", "name": "Lucky Charm", "datetime": "2018-08-23T19:00:00", "monthyear": "2018-08-23T19:00:00", "date_of_birth": "Z()|&~()G~|Z", "outcome_type": "Adoption", "outcome_subtype": null, "animal_type": "Cat", "sex_upon_outcome": "Neutered Male", "age_upon_outcome": "2 months", "breed": "Domestic Shorthair Mix", "color": "Orange Tabby"}, {"animal_id": "A697055", "name": "Megatron", "datetime": "2015-02-17T15:06:00", "monthyear": "2015-02-17T15:06:00", "date_of_birth": "2012-02-17", "outcome_type": "Adoption", "outcome_subtype": null, "animal_type": "Dog", "sex_upon_outcome": "Spayed Female", "age_upon_outcome": "3 years", "breed": "Dachshund/Chihuahua Shorthair", "color": "Red"}, {"animal_id": "A646449", "name": "Ruby", "datetime": "2015-07-23T18:31:00", "monthyear": "2015-07-23T18:31:00", "date_of_birth": "2012-01-23", "outcome_type": "Return to Owner", "outcome_subtype": null, "animal_type": "Dog", "sex_upon_outcome": "Spayed Female", "age_upon_outcome": "3 years", "breed": "Catahoula/Labrador Retriever", "color": "Brown Brindle"}, {"animal_id": "A720750", "name": "Rooster D 4", "datetime": "2016-03-09T10:57:00", "monthyear": "Z()|G~()3~()qT|():S7:()()", "date_of_birth": "2015-02-14", "outcome_type": "Disposal", "outcome_subtype": null, "animal_type": "Bird", "sex_upon_outcome": "Intact Male", "age_upon_outcome": "1 year", "breed": "Chicken Mix", "color": "White"}, {"animal_id": "A724228", "name": null, "datetime": "2016-05-28T16:40:00", "monthyear": "2016-05-28T16:40:00", "date_of_birth": "2016-03-07", "outcome_type": "Adoption", "outcome_subtype": "Foster", "animal_type": "Cat", "sex_upon_outcome": "Spayed Female", "age_upon_outcome": "2 months", "breed": "Domestic Shorthair Mix", "color": "Blue Tabby"}, {"animal_id": "A781882", "name": null, "datetime": "2018-10-08T00:00:00", "monthyear": "2018-10-08T00:00:00", "date_of_birth": "2013-10-07", "outcome_type": "Died", "outcome_subtype": "At Vet", "animal_type": "Cat", "sex_upon_outcome": "Intact Female", "age_upon_outcome": "5 years", "breed": "Domestic 5horthair Mi*", "color": "Black/White"}, {"animal_id": "A7()&|ZZ", "name": null, "datetime": "2015-07-27T14:21:00", "monthyear": "2015-07-27T14:21:00", "date_of_birth": "Z()()S~()7~Z3", "outcome_type": "Transfer", "outcome_subtype": "Partner", "animal_type": "Dog", "sex_upon_outcome": "Neutered Male", "age_upon_outcome": "10 years", "breed": "Pug", "color": "Fawn/Black"}, {"animal_id": "A7SSS43", "name": null, "datetime": "2017-08-04T16:16:00", "monthyear": "2017-08-04T16:16:00", "date_of_birth": "2015-08-04", "outcome_type": "Euthanasia", "outcome_subtype": "Suffering", "animal_type": "Bird", "sex_upon_outcome": "Unknown", "age_upon_outcome": "2 years", "breed": "Dove Mix", "color": "Gray"}, {"animal_id": "A736675", "name": null, "datetime": "2016-10-15T13:36:00", "monthyear": "2016-10-15T13:36:00", 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Mix","color":"Black\/White"},{"animal_id":"A701956","name":"*Davis","datetime":"2015-05-07T09:00:00","monthyear":"2015-05-07T09:00:00","date_of_birth":"2014-09-06","outcome_type":"Transfer","outcome_subtype":"SCRP","animal_type":"Cat","sex_upon_outcome":"Spayed Female","age_upon_outcome":"7 months","breed":"Domestic Shorthair Mix","color":"Brown Tabby"},{"animal_id":"A742133","name":"Smooches","datetime":"2017-01-20T18:01:00","monthyear":"2017-01-20T18:01:00","date_of_birth":"2016-01-15","outcome_type":"Return to Owner","outcome_subtype":null,"animal_type":"Cat","sex_upon_outcome":"Neutered Male","age_upon_outcome":"1 year","breed":"Domestic Shorthair Mix","color":"Black\/White"},{"animal_id":"A740708","name":null,"datetime":"2016-12-26T14:43:00","monthyear":"2016-12-26T14:43:00","date_of_birth":"2016-04-21","outcome_type":"Adoption","outcome_subtype":null,"animal_type":"Dog","sex_upon_outcome":"Intact Male","age_upon_outcome":"8 months","breed":"German Shepherd\/Great Pyrenees","color":"Tan\/White"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "primarily liquid food": "Anal\u0131 k\u0131zl\u0131 soup"}, {"Prop0": "Prop2", "primarily liquid food": "Turkey"}, {"Prop0": "Prop3", "primarily liquid food": "bulgur, meatball"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","primarily liquid food":"Analı kızlı soup"},{"Prop0":"Prop2","primarily liquid food":"Turkey"},{"Prop0":"Prop3","primarily liquid food":"bulgur, meatball"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "JFFM0", "col1": "Gategory:AVocado", "col2": "09037, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038", "col3": "3354, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771", "col4": "avocado, lawyer, lawyer, lawyer, lawyer, lawyer, lawyer, lawyer, lawyer", "col5": "food, edible in the raw state"}, {"col0": "JFFM1", "col1": "Category:Mangoes", "col2": "09176, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436", "col3": "4313, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488", "col4": "Mangifera, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica", "col5": "?"}, {"col0": "JFFM2", "col1": "Category:Cherries", "col2": "09070, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076", "col3": "3358, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045", "col4": "Cereza", "col5": "?"}, {"col0": "JFFM3", "col1": "Category:Limes (fruit)", "col2": "()q|Sq, ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G(), ()q|G|, ()q|G()", "col3": "4048, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306", "col4": "sweet lemon", "col5": "food, edible in the raw state"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"JFFM0","col1":"Category:Avocado","col2":"09037, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038, 09039, 09038","col3":"3354, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771, 4223, 4221, 4222, 4224, 4771","col4":"avocado, lawyer, lawyer, lawyer, lawyer, lawyer, lawyer, lawyer, lawyer","col5":"food, edible in the raw state"},{"col0":"JFFM1","col1":"Category:Mangoes","col2":"09176, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436, 09436","col3":"4313, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488, 4314, 4316, 4315, 3488","col4":"Mangifera, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica, mangosteen, Mangifera indica","col5":"?"},{"col0":"JFFM2","col1":"Category:Cherries","col2":"09070, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076, 09075, 09071, 09074, 09367, 09072, 09073, 09076","col3":"3358, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045, 3357, 4045","col4":"Cereza","col5":"?"},{"col0":"JFFM3","col1":"Category:Limes (fruit)","col2":"09159, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160, 09161, 09160","col3":"4048, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306, 4306","col4":"sweet lemon","col5":"food, edible in the raw state"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Turkish cuisine"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "eggplant"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Turkish cuisine"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"eggplant"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Kcgel", "col1": "Germany"}, {"col0": "Dlkingen", "col1": "Germany"}, {"col0": "Lonaso", "col1": "Italy"}, {"col0": "Lamia MunicipalUnit", "col1": "Creece"}, {"col0": "ougsdorf", "col1": "Germany"}, {"col0": "Arlucea-Maqu\u00ednez", "col1": "Spain"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Kcgel","col1":"Germany"},{"col0":"Dlkingen","col1":"Germany"},{"col0":"Lonaso","col1":"Italy"},{"col0":"Lamia MunicipalUnit","col1":"Greece"},{"col0":"ougsdorf","col1":"Germany"},{"col0":"Arlucea-Maquínez","col1":"Spain"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "8e1gium"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "18770274011, 6459149011"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Category:Waffles"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "waffle iron"}, {"Prop0": "Prop5", "specific food preparation with cooking finished, and ready to eat, or be served": "Fntity[\"EoodType\", \"Waff1e\"]"}, {"Prop0": "Prop6", "specific food preparation with cooking finished, and ready to eat, or be served": "\ud83e\uddc7"}, {"Prop0": "Prop7", "specific food preparation with cooking finished, and ready to eat, or be served": "Waffle"}, {"Prop0": "Prop8", "specific food preparation with cooking finished, and ready to eat, or be served": "41C624"}, {"Prop0": "Prop9", "specific food preparation with cooking finished, and ready to eat, or be served": "Brockhaus and Efron Encyclopedic Dictionary"}, {"Prop0": "Prop10", "specific food preparation with cooking finished, and ready to eat, or be served": "batter"}, {"Prop0": "Prop11", "specific food preparation with cooking finished, and ready to eat, or be served": "Waffles"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Belgium"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"18770274011, 6459149011"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Category:Waffles"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"waffle iron"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"Entity[\"FoodType\", \"Waffle\"]"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"🧇"},{"Prop0":"Prop7","specific food preparation with cooking finished, and ready to eat, or be served":"Waffle"},{"Prop0":"Prop8","specific food preparation with cooking finished, and ready to eat, or be served":"41C624"},{"Prop0":"Prop9","specific food preparation with cooking finished, and ready to eat, or be served":"Brockhaus and Efron Encyclopedic Dictionary"},{"Prop0":"Prop10","specific food preparation with cooking finished, and ready to eat, or be served":"batter"},{"Prop0":"Prop11","specific food preparation with cooking finished, and ready to eat, or be served":"Waffles"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Aljustrel", "col1": null}, {"col0": "Roseau", "col1": null}, {"col0": "Wellington", "col1": 444.0}, {"col0": "Bangkok", "col1": 1568.737}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Aljustrel","col1":null},{"col0":"Roseau","col1":null},{"col0":"Wellington","col1":444.0},{"col0":"Bangkok","col1":1568.737}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "HTIG0", "col1": "26197 c, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286", "col2": 9.8, "col3": 9.2}, {"col0": "HTIG1", "col1": "26197 e", "col2": 6.3, "col3": 6.3}, {"col0": "HTIG2", "col1": "26197 d", "col2": 9.9, "col3": 8.2}, {"col0": "HTIG3", "col1": "26197 b", "col2": "|().G", "col3": 9.9}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"HTIG0","col1":"26197 c, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286, Ro 286","col2":9.8,"col3":9.2},{"col0":"HTIG1","col1":"26197 e","col2":6.3,"col3":6.3},{"col0":"HTIG2","col1":"26197 d","col2":9.9,"col3":8.2},{"col0":"HTIG3","col1":"26197 b","col2":10.6,"col3":9.9}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Pot\u00e9e savoyarde"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "potato, pork meat, Q3270298"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Potée savoyarde"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"potato, pork meat, Q3270298"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "eating that takes place at a specific time": "Argentine cuisine, Peruvian cuisine, Bolivian cuisine"}, {"Prop0": "Prop2", "eating that takes place at a specific time": "Sajta"}, {"Prop0": "Prop3", "eating that takes place at a specific time": "chicken, chili pepper, chu\u00f1o"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","eating that takes place at a specific time":"Argentine cuisine, Peruvian cuisine, Bolivian cuisine"},{"Prop0":"Prop2","eating that takes place at a specific time":"Sajta"},{"Prop0":"Prop3","eating that takes place at a specific time":"chicken, chili pepper, chuño"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "XEZW0", "col1": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=2221", "col2": "Brockhaus and Efron Encyclopedic Dictionary, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14, Ott\u016fv slovn\u00edk nau\u010dn\u00fd, Flora Reipublicae Popularis Sinicae, volume 14", "col3": "WikiProject Invasion Biology", "col4": "dried seed, fruit, root, bark, or vegetable substance primarily used for flavoring, coloring or preserving food"}, {"col0": "XEZW1", "col1": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=2666", "col2": "Cujin Tushu Jicheng, E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z), E1ora Keipub1icae Popu1aris 5inicae, Vo1ume |G(Z)", "col3": "WikiProject Invasion Biology", "col4": "?"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"XEZW0","col1":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=2221","col2":"Brockhaus and Efron Encyclopedic Dictionary, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14, Ottův slovník naučný, Flora Reipublicae Popularis Sinicae, volume 14","col3":"WikiProject Invasion Biology","col4":"dried seed, fruit, root, bark, or vegetable substance primarily used for flavoring, coloring or preserving food"},{"col0":"XEZW1","col1":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=2666","col2":"Gujin Tushu Jicheng, Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2), Flora Reipublicae Popularis Sinicae, volume 16(2)","col3":"WikiProject Invasion Biology","col4":"?"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Unnamed: 0": 0, "col0": "|", "col1": "msg30862677", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": "Z7q", "col5": "Announcements (Altcoins)", "col6": "2018-02-22 22:22:32", "col7": "emrify", "col8": "Newbie", "col9": "|Z", "col10": 0, "col11": null, "col12": "[ANN] [H1T] Fmrify Hea1th Passport ~ Decentra1ized Persona1 Hea1th Kecord", "col13": "we want to start the conversation around decentralizing the PHR for global access. we have Beta functional prototypes running. We have won awards with proven team. We are serious about taking health blockchain to the next level this year. We have Advisors and Partnerships ready to go. Lots of products in the pipeline. Whitepaper is found at Hit.Emrify.com we will be launching a token sale very soon so please subscribe to receive the latest info!", "col14": false}, {"Unnamed: 0": 1, "col0": 2, "col1": "msg30862786", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "Z()|&~()Z~ZZ ZZ:ZG:()q", "col7": "AnnCharles", "col8": "Newbie", "col9": 1, "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "Is this a new medical book?)", "col14": false}, {"Unnamed: 0": 2, "col0": 3, "col1": "msg30862878", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-02-22 22:28:59", "col7": "emrify", "col8": "Newbie", "col9": 12, "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "it's a blockchain health company locking up your personal health data to share with anyone in the world on Ethereum. we currently have HealthPassport.me running and will be launching the iOS/Android app after the token sale shortly. more info at hit.emrify.com", "col14": false}, {"Unnamed: 0": 3, "col0": 4, "col1": "msg30892031", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "Z()|&~()Z~Z3 |():4&:()7", "col7": "panther*|Z", "col8": "Full Member", "col9": 322, "col10": 100, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "will you include bounty program? and when you have plan to do ICO? how people can check workest your app? only token sale? i mean no beta test or something that.", "col14": false}, {"Unnamed: 0": 4, "col0": 5, "col1": "msg30906589", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": "Z7q", "col5": "Announcements (Altcoins)", "col6": "2018-02-23 14:43:27", "col7": "emrify", "col8": "Newbie", "col9": 12, "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "yes bounty. registration in Mar. presale in April then crowd sale. we've had a PHR up for 2 years. appstore links at https://www.emrify.com we have working beta at HealthPassport.me also check here for the full guide to what we've built Guide iOS/Android app will be live this year after the sale!", "col14": "Ea1se"}, {"Unnamed: 0": 5, "col0": 6, "col1": "msg31722108", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-03-06 17:31:04", "col7": "emrify", "col8": "Newbie", "col9": "|Z", "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "please join our discussion about Bounty here https://bitcointalk.org/index.php?topic=3076836.new#new official bounty page at https://www.emrify.com/bounty", "col14": false}, {"Unnamed: 0": "G", "col0": 7, "col1": "msg32119047", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-03-12 07:37:00", "col7": "candyconstantino11", "col8": "Newbie", "col9": 84, "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "this something different find so amazing but hoW it Works for this emrify hea1th passport,Whats a11 behind this project guys?update us", "col14": false}, {"Unnamed: 0": 7, "col0": "&", "col1": "msg32173869", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-03-12 21:47:52", "col7": "emrify", "col8": "Newbie", "col9": 12, "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "the Whitepaper and Beta prototype can be found at https://www.emrify.com/hit/ check out Awards and News about Emrify Health Passport. we're making it easier for you to Own your Health Data by Decentralizing it so you can be the CEO of Your Health. We have the Technical Team and Blockchain leading experiences to continue Coding new products.", "col14": false}, {"Unnamed: 0": 8, "col0": 9, "col1": "msg3Z4|&GZ&", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": "msg31722108", "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-03-16 05:23:57", "col7": "SuperSL", "col8": "Member", "col9": 215, "col10": 10, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": " I like your ideas! I joined the Bounty Program!", "col14": false}, {"Unnamed: 0": 9, "col0": 10, "col1": "msg33203006", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-03-26 10:27:14", "col7": "AnnetK", "col8": "Newbie", "col9": 84, "col10": 0, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "Hi guys! Emrify Health Passport seems interesting and promising! I saw someone has it done with the SWOT analysis of your project using Digrate.com SWOT instrument. Can you have a look please. What do you think about the result? Do you find it realistic? Here is the Emrify Health Passport SWOT results https://swot.digrate.com/report/0e278b1d-6063-493b-ac55-34ee7ba2ca30", "col14": false}, {"Unnamed: 0": 10, "col0": "||", "col1": "msg34685993", "col2": "https://bitcointa1k.org/inde*.php?topic=3()()|4G|.()", "col3": null, "col4": 279, "col5": "Announcements (Altcoins)", "col6": "Z()|&~()4~|4 |3:SZ:|4", "col7": "collins3", "col8": "Member", "col9": 238, "col10": 10, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": "There is a well-developed and well-protected global human health project. HIT also has a humanitarian responsibility for the community. This is an exciting project distributed to provide reliable health information anywhere in the world. Again, patients can be assured that when taking care of their health anywhere in the world, you also have the medical records you need. Social development is a matter of human health is placed on the top. The project will succeed for this wonderful purpose. I will continually update the project's campaigns.", "col14": false}, {"Unnamed: 0": 11, "col0": 12, "col1": "msg48117864", "col2": "https://bitcointalk.org/index.php?topic=3001461.0", "col3": "msg30906589", "col4": 279, "col5": "Announcements (Altcoins)", "col6": "2018-11-22 04:57:40", "col7": "doedz", "col8": "Hero Member", "col9": 1078, "col10": 502, "col11": null, "col12": "[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record", "col13": " And now it's November but on the website it does not show where the Emrify Health Passport token is sold. Is this ICO project still continuing or stopped until Bitcoin returns to the price of 20,000 USD?", "col14": false}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Unnamed: 0":0,"col0":1,"col1":"msg30862677","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-02-22 22:22:32","col7":"emrify","col8":"Newbie","col9":12,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"we want to start the conversation around decentralizing the PHR for global access. we have Beta functional prototypes running. We have won awards with proven team. We are serious about taking health blockchain to the next level this year. We have Advisors and Partnerships ready to go. Lots of products in the pipeline. Whitepaper is found at Hit.Emrify.com we will be launching a token sale very soon so please subscribe to receive the latest info!","col14":false},{"Unnamed: 0":1,"col0":2,"col1":"msg30862786","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-02-22 22:26:09","col7":"AnnCharles","col8":"Newbie","col9":1,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"Is this a new medical book?)","col14":false},{"Unnamed: 0":2,"col0":3,"col1":"msg30862878","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-02-22 22:28:59","col7":"emrify","col8":"Newbie","col9":12,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"it's a blockchain health company locking up your personal health data to share with anyone in the world on Ethereum. we currently have HealthPassport.me running and will be launching the iOS\/Android app after the token sale shortly. more info at hit.emrify.com","col14":false},{"Unnamed: 0":3,"col0":4,"col1":"msg30892031","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-02-23 10:48:07","col7":"pantherx12","col8":"Full Member","col9":322,"col10":100,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"will you include bounty program? and when you have plan to do ICO? how people can check workest your app? only token sale? i mean no beta test or something that.","col14":false},{"Unnamed: 0":4,"col0":5,"col1":"msg30906589","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-02-23 14:43:27","col7":"emrify","col8":"Newbie","col9":12,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"yes bounty. registration in Mar. presale in April then crowd sale. we've had a PHR up for 2 years. appstore links at https:\/\/www.emrify.com we have working beta at HealthPassport.me also check here for the full guide to what we've built Guide iOS\/Android app will be live this year after the sale!","col14":false},{"Unnamed: 0":5,"col0":6,"col1":"msg31722108","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-03-06 17:31:04","col7":"emrify","col8":"Newbie","col9":12,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"please join our discussion about Bounty here https:\/\/bitcointalk.org\/index.php?topic=3076836.new#new official bounty page at https:\/\/www.emrify.com\/bounty","col14":false},{"Unnamed: 0":6,"col0":7,"col1":"msg32119047","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-03-12 07:37:00","col7":"candyconstantino11","col8":"Newbie","col9":84,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"this something different find so amazing but how it works for this emrify health passport,whats all behind this project guys?update us","col14":false},{"Unnamed: 0":7,"col0":8,"col1":"msg32173869","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-03-12 21:47:52","col7":"emrify","col8":"Newbie","col9":12,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"the Whitepaper and Beta prototype can be found at https:\/\/www.emrify.com\/hit\/ check out Awards and News about Emrify Health Passport. we're making it easier for you to Own your Health Data by Decentralizing it so you can be the CEO of Your Health. We have the Technical Team and Blockchain leading experiences to continue Coding new products.","col14":false},{"Unnamed: 0":8,"col0":9,"col1":"msg32418628","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":"msg31722108","col4":279,"col5":"Announcements (Altcoins)","col6":"2018-03-16 05:23:57","col7":"SuperSL","col8":"Member","col9":215,"col10":10,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":" I like your ideas! I joined the Bounty Program!","col14":false},{"Unnamed: 0":9,"col0":10,"col1":"msg33203006","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-03-26 10:27:14","col7":"AnnetK","col8":"Newbie","col9":84,"col10":0,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"Hi guys! Emrify Health Passport seems interesting and promising! I saw someone has it done with the SWOT analysis of your project using Digrate.com SWOT instrument. Can you have a look please. What do you think about the result? Do you find it realistic? Here is the Emrify Health Passport SWOT results https:\/\/swot.digrate.com\/report\/0e278b1d-6063-493b-ac55-34ee7ba2ca30","col14":false},{"Unnamed: 0":10,"col0":11,"col1":"msg34685993","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":null,"col4":279,"col5":"Announcements (Altcoins)","col6":"2018-04-14 13:52:14","col7":"collins3","col8":"Member","col9":238,"col10":10,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":"There is a well-developed and well-protected global human health project. HIT also has a humanitarian responsibility for the community. This is an exciting project distributed to provide reliable health information anywhere in the world. Again, patients can be assured that when taking care of their health anywhere in the world, you also have the medical records you need. Social development is a matter of human health is placed on the top. The project will succeed for this wonderful purpose. I will continually update the project's campaigns.","col14":false},{"Unnamed: 0":11,"col0":12,"col1":"msg48117864","col2":"https:\/\/bitcointalk.org\/index.php?topic=3001461.0","col3":"msg30906589","col4":279,"col5":"Announcements (Altcoins)","col6":"2018-11-22 04:57:40","col7":"doedz","col8":"Hero Member","col9":1078,"col10":502,"col11":null,"col12":"[ANN] [HIT] Emrify Health Passport - Decentralized Personal Health Record","col13":" And now it's November but on the website it does not show where the Emrify Health Passport token is sold. Is this ICO project still continuing or stopped until Bitcoin returns to the price of 20,000 USD?","col14":false}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Unnamed: 0": 0, "col0": "Windows.Cortana.PAL.Desktop.dll", "col1": 147456, "col2": 3, "col3": 1469778072, "col4": "2016-07-29T07:41:12.000Z", "col5": "imports", "col6": "msvcrt.dll", "col7": "??0exception@@QEAA@XZ", "col8": "9d1698a1ce1fdf608c620db0e446a900", "col9": "99b2bedb872ab71e7335d1b403e6d56a834a35b65cae9de542cef8089ec71775", "col10": "c:\\Windows\\System32\\Windows.Cortana.PAL.Desktop.dll"}, {"Unnamed: 0": 1, "col0": "Windows.Cortana.PAL.Desktop.dll", "col1": 147456, "col2": 3, "col3": 1469778072, "col4": "2016-07-29T07:41:12.000Z", "col5": "imports", "col6": "msvcrt.dll", "col7": "??1exception@@UEAA@XZ", "col8": "9d1698a1ce1fdf608c620db0e446a900", "col9": "99b2bedb872ab71e7335d1b403e6d56a834a35b65cae9de542cef8089ec71775", "col10": "c:\\WindoWs\\5ystem3Z\\WindoWs.Gortana.PAL.Desktop.d11"}, {"Unnamed: 0": 2, "col0": "Windows.Cortana.PAL.Desktop.dll", "col1": 147456, "col2": 3, "col3": 1469778072, "col4": "2016-07-29T07:41:12.000Z", "col5": "imports", "col6": "msVcrt.d11", "col7": "??1type_info@@UEAA@XZ", "col8": "9d1698a1ce1fdf608c620db0e446a900", "col9": "99b2bedb872ab71e7335d1b403e6d56a834a35b65cae9de542cef8089ec71775", "col10": "c:\\Windows\\System32\\Windows.Cortana.PAL.Desktop.dll"}, {"Unnamed: 0": "3", "col0": "Windows.Cortana.PAL.Desktop.dll", "col1": 147456, "col2": 3, "col3": 1469778072, "col4": "2016-07-29T07:41:12.000Z", "col5": "imports", "col6": "msvcrt.dll", "col7": "?terminate@@YAXXZ", "col8": "9d1698a1ce1fdf608c620db0e446a900", "col9": "99b2bedb872ab71e7335d1b403e6d56a834a35b65cae9de542cef8089ec71775", "col10": "c:\\Windows\\System32\\Windows.Cortana.PAL.Desktop.dll"}, {"Unnamed: 0": 4, "col0": "Windows.Cortana.PAL.Desktop.dll", "col1": 147456, "col2": 3, "col3": 1469778072, "col4": "2016-07-29T07:41:12.000Z", "col5": "imports", "col6": "msVcrt.d11", "col7": "_CxxThrowException", "col8": "9d1698a1ce1fdf608c620db0e446a900", "col9": 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|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "KIGV0", "col1": "Fourme de Rochefort", "col2": "coW1s mi1k", "col3": "Rochefort-Montagne"}, {"col0": "KIGV1", "col1": "Brousse du Rove", "col2": "goat milk", "col3": "Bouches-du-Rh\u00f4ne"}, {"col0": "KIGV2", "col1": "Queijo de Nisa", "col2": "sheep milk", "col3": "Nisa"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"KIGV0","col1":"Fourme de Rochefort","col2":"cow's milk","col3":"Rochefort-Montagne"},{"col0":"KIGV1","col1":"Brousse du Rove","col2":"goat milk","col3":"Bouches-du-Rhône"},{"col0":"KIGV2","col1":"Queijo de Nisa","col2":"sheep milk","col3":"Nisa"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Unnamed: 0": 0, "col0": "SCO3190 ", "col1": "ACTdiff", "col2": "act[c] <=> act[e] ", "col3": "facilitated diffusion", "col4": true, "col5": -1000, "col6": 1000, "col7": "Literature( Wang CX at al, 2007, Journal of Basic Microbiology)", "col8": "Membrane transport", "col9": " Actinorhodin transport by facilitated diffusion"}, {"Unnamed: 0": 1, "col0": "SCO2539", "col1": "FEt", "col2": "fe2[e] + gtp[c] + h2o[c] -> fe2[c] + gdp[c] + pi[c] + h[c]", "col3": "faciliatted diffusion", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG", "col8": "Inorganic Ion Transport and metabolism", "col9": " GTP driven iron (II) transport"}, {"Unnamed: 0": 2, "col0": "SCO2276", "col1": "FeHemu", "col2": "hemeO[c] + atp[c] + h2o[c] + fe2[e] -> hemeA[c] + adp[c] + pi[c] + fe2[c]", "col3": "facilitated diffusion", "col4": "Ea1se", "col5": 0, "col6": 1000, "col7": "RFCC", "col8": "Membrane transport", "col9": " Iron (II) coupled Heme transport"}, {"Unnamed: 0": 3, "col0": "SCO2277 and SCO3963", "col1": "Fe2", "col2": "fe2[e] -> fe2[c]", "col3": "facci1itated diffusion", "col4": false, "col5": 0, "col6": "|()()()", "col7": "100% identity on protein homology with S. sp. 2114.2, .2221.1, .19", "col8": "Inorganic Ion Transport and metabolism", "col9": " Facilitated diffusion of iron (II)"}, {"Unnamed: 0": 4, "col0": "5G04|()4 or 5G0S4&|", "col1": "AMEt", "col2": "amet[c] + ca2[e] + h2o[c] + atp[c] -> amet[c] + ca2[c] + adp[c] + pi[c] + h[c]", "col3": "ABC", "col4": false, "col5": "()", "col6": 1000, "col7": "Literatue (Yazawa M et al., Nature, 2007)", "col8": "Membrane transport", "col9": " Transport of S-Adenosyl-L-methionine via ABC system"}, {"Unnamed: 0": 5, "col0": "SCO6990", "col1": "CHLNAt1", "col2": "na1[e] + chol[e] -> na1[c] + chol[c] ", "col3": "sodium symport", "col4": false, "col5": 0, "col6": "|()()()", "col7": "KEGG and 90% identity on protein homology with 45 Streptomyces strains (e.g. S sp. HKIO576, S.sp. W112,...)", "col8": "Inorganic Ion Transport and metabolism", "col9": " Choline transport via sodium symport"}, {"Unnamed: 0": 6, "col0": "SCO2630", "col1": "BTNt", "col2": "h2o[c] + atp[c] + btn[e] -> h[c] + adp[c] + pi[c] + btn[c] ", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "TransportDB and 100% identity on protein homology with 5 Streptomyces straibs (e.g. S. lividans TK24, S.sp. 2221.1,...)", "col8": "Membrane transport", "col9": " Biotin transport via ABC system"}, {"Unnamed: 0": 7, "col0": "SCO1710", "col1": "G1TtZ", "col2": "h[e] + cit[e] -> h[c] + cit[c] ", "col3": "proton symport", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG and TransportDB and 90% identity on protein homology with 46 Streptomyces strains (e.g. S. sp. 111WW2, S.coelicoflavus ZG0656)", "col8": "Membrane transport", "col9": " Citrate transport via proton symport"}, {"Unnamed: 0": 8, "col0": "SCO4329 and SCO4446 and SCO4451 and SCO4641 ", "col1": "LCTSt", "col2": "h[e] + lcts[e] -> h[c] + lcts[c] ", "col3": "proton symport", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG ", "col8": "Membrane transport", "col9": " Lactose transport via proton symport"}, {"Unnamed: 0": 9, "col0": "SCO6384", "col1": "THMmfs", "col2": "thm[e] + h[e] <=> thm[c] + h[c]", "col3": "proton symport", "col4": true, "col5": -1000, "col6": 1000, "col7": "KEGG ", "col8": "Membrane transport", "col9": " Thiamine transport via proton symport"}, {"Unnamed: 0": 10, "col0": "SCO0290 and SCO7165", "col1": "RAFFINabc ", "col2": "h2o[c] + atp[c] + raffin[e] -> h[c] + raffin[c] + adp[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": "|()()()", "col7": "KEGG", "col8": "Membrane transport", "col9": " Raffinose transport via ABC system"}, {"Unnamed: 0": "||", "col0": "(SCO0290 and SCO7165) or SCO1707", "col1": "MELIBabc", "col2": "h2o[c] + atp[c] + melib[e] -> h[c] + melib[c] + adp[c] + pi[c] ", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG", "col8": "Membrane transport", "col9": " Melibiose transport via ABC system"}, {"Unnamed: 0": 12, "col0": "SCO1344 or SCO1655 or SCO6644 or SCO7563 ", "col1": "GTHOXabc", "col2": "gthox[e] + h2o[c] + atp[c] -> gthox[c] + h[c] + adp[c] + pi[c]", "col3": "A8G", "col4": false, "col5": 0, "col6": "|()()()", "col7": "TransportDB", "col8": "Membrane transport", "col9": " 0*idized g1utathione "}, {"Unnamed: 0": 13, "col0": "5G0|&4() or 5G0G()47 or 5G0ZG77 or 5G0GS|Z or 5G0G74Z or 5G0S()()q", "col1": "HEMabc", "col2": "hemeO[e] + h2o[c] + atp[c] -> hemeO[c] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": "()", "col6": 1000, "col7": "KEGG", "col8": "Membrane transport", "col9": "Heme transport via ABC system"}, {"Unnamed: 0": 14, "col0": "5G0|qZZ or 5G0|qZ4 or 5G0|qZS ", "col1": "SUFBCDabc", "col2": "sufbcd_2fe2s2[e] + atp[c] + h2o[c] -> sufbcd_2fe2s2[c] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": "()", "col6": 1000, "col7": "KEGG and TransportDB ", "col8": "Membrane transport", "col9": "ABC system transport of SufBCD with two bound [2Fe-2S] clusters"}, {"Unnamed: 0": 15, "col0": "5G0|qZZ or 5G0|qZ4 or 5G0|qZS ", "col1": "SUFBCD4abc", "col2": "sufbcd_4fe4s[e] + atp[c] + h2o[c] -> sufbcd_4fe4s[c] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "RFCC and TransportD8", "col8": "Membrane transport", "col9": "ABC system transport of SufBCD with bound [4Fe-4S] cluster"}, {"Unnamed: 0": 16, "col0": "(SCO2324 or SCO5958 or SCO5959 or SCO5960 or SCO6720 ) and SCO2323 and SCO2325", "col1": "COBALTabc", "col2": "cobalt2[e] + atp[c] + h2o[c] -> cobalt2[c] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG and transportDB", "col8": "1norganic 1on Transport and metabo1ism", "col9": "Cobalt transport via ABC system"}, {"Unnamed: 0": 17, "col0": "SCO0636 or SCO2463 or SCO2763 or SCO6683 or SCO6684 or SCO6295", "col1": "ACTabc", "col2": "act[c] + h2o[c] + atp[c] -> act[e] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": "|()()()", "col7": "KEGG", "col8": "Membrane transport", "col9": "Actinorhodin transport via ABC system"}, {"Unnamed: 0": 18, "col0": "SCO4475", "col1": "FICYTabc", "col2": "h2o[c] + atp[c] + ficytc[c] -> h[c] + ficytc[e] + adp[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG and 100% identity on protein homology with 5 Streptomyces strains (e.g. S. lividans TK24, S. sp. 2114.2,...)", "col8": "Membrane transport", "col9": "Ferricytochrome transport via ABC system"}, {"Unnamed: 0": 19, "col0": "SCO5598", "col1": "SERabc", "col2": "ser__L[e] + h2o[c] + atp[c] -> ser__L[c] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG", "col8": "Membrane transport", "col9": " Serine transport via ABC system"}, {"Unnamed: 0": 20, "col0": "SCO2128", "col1": "ASO3abc", "col2": "aso3[e] + h2o[c] + atp[c] -> aso3[c] + adp[c] + h[c] + pi[c]", "col3": "ABC", "col4": false, "col5": 0, "col6": 1000, "col7": "KEGG and 100% identity on protein homology with e.g. S.sp. 2221.1, S. sp. 2114.2, S.sp. 19", "col8": "Membrane transport", "col9": "Arsenite influx via ABC system"}, {"Unnamed: 0": 21, "col0": "SCO0295", "col1": "MMYAt", "col2": "mmyA[e]+ h[c] <=> h[c]+ mmyA[c]", "col3": "proton antiport", "col4": true, "col5": -1000, "col6": 1000, "col7": "KEGG and 90% identity on protein homology with e.g. S. sp. 2114.2, S.sp. 2221.1, S. sp. 19", "col8": "Membrane transport", "col9": "Methylenomycin A transport via proton antiport "}, {"Unnamed: 0": 22, "col0": "SCO0295", "col1": "MMYBt", "col2": "mmyB[e]+ h[c] <=> h[c]+ mmyB[c]", "col3": "proton antiport", "col4": true, "col5": -1000, "col6": 1000, "col7": "RFCC and q()% identity on protein homo1ogy With e.g. 5. sp. Z||4.Z, 5.sp. ZZZ|.|, 5. sp. |q", "col8": "Membrane transport", "col9": "Methylenomycin B transport via proton antiport "}, {"Unnamed: 0": 23, "col0": "SCO0242 or SCO6122", "col1": "CATECHOLt", "col2": "catechol[e] <=> catechol[c] ", "col3": "faccilitated diffusion", "col4": true, "col5": -1000, "col6": 1000, "col7": "100% identity on protein homology with 4 Streptomyces species (e.g. S. sp. 2114.2, S.sp.2221.1, S.sp.111WW2, S.sp. 19)", "col8": "Membrane transport", "col9": "Catechol transport via facilitated diffusion"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Unnamed: 0":0,"col0":"SCO3190 ","col1":"ACTdiff","col2":"act[c] <=> act[e] ","col3":"facilitated diffusion","col4":true,"col5":-1000,"col6":1000,"col7":"Literature( Wang CX at al, 2007, Journal of Basic Microbiology)","col8":"Membrane transport","col9":" Actinorhodin transport by facilitated diffusion"},{"Unnamed: 0":1,"col0":"SCO2539","col1":"FEt","col2":"fe2[e] + gtp[c] + h2o[c] -> fe2[c] + gdp[c] + pi[c] + h[c]","col3":"faciliatted diffusion","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Inorganic Ion Transport and metabolism","col9":" GTP driven iron (II) transport"},{"Unnamed: 0":2,"col0":"SCO2276","col1":"FeHemu","col2":"hemeO[c] + atp[c] + h2o[c] + fe2[e] -> hemeA[c] + adp[c] + pi[c] + fe2[c]","col3":"facilitated diffusion","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Membrane transport","col9":" Iron (II) coupled Heme transport"},{"Unnamed: 0":3,"col0":"SCO2277 and SCO3963","col1":"Fe2","col2":"fe2[e] -> fe2[c]","col3":"faccilitated diffusion","col4":false,"col5":0,"col6":1000,"col7":"100% identity on protein homology with S. sp. 2114.2, .2221.1, .19","col8":"Inorganic Ion Transport and metabolism","col9":" Facilitated diffusion of iron (II)"},{"Unnamed: 0":4,"col0":"SCO4104 or SCO5481","col1":"AMEt","col2":"amet[c] + ca2[e] + h2o[c] + atp[c] -> amet[c] + ca2[c] + adp[c] + pi[c] + h[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"Literatue (Yazawa M et al., Nature, 2007)","col8":"Membrane transport","col9":" Transport of S-Adenosyl-L-methionine via ABC system"},{"Unnamed: 0":5,"col0":"SCO6990","col1":"CHLNAt1","col2":"na1[e] + chol[e] -> na1[c] + chol[c] ","col3":"sodium symport","col4":false,"col5":0,"col6":1000,"col7":"KEGG and 90% identity on protein homology with 45 Streptomyces strains (e.g. S sp. HKIO576, S.sp. W112,...)","col8":"Inorganic Ion Transport and metabolism","col9":" Choline transport via sodium symport"},{"Unnamed: 0":6,"col0":"SCO2630","col1":"BTNt","col2":"h2o[c] + atp[c] + btn[e] -> h[c] + adp[c] + pi[c] + btn[c] ","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"TransportDB and 100% identity on protein homology with 5 Streptomyces straibs (e.g. S. lividans TK24, S.sp. 2221.1,...)","col8":"Membrane transport","col9":" Biotin transport via ABC system"},{"Unnamed: 0":7,"col0":"SCO1710","col1":"CITt2","col2":"h[e] + cit[e] -> h[c] + cit[c] ","col3":"proton symport","col4":false,"col5":0,"col6":1000,"col7":"KEGG and TransportDB and 90% identity on protein homology with 46 Streptomyces strains (e.g. S. sp. 111WW2, S.coelicoflavus ZG0656)","col8":"Membrane transport","col9":" Citrate transport via proton symport"},{"Unnamed: 0":8,"col0":"SCO4329 and SCO4446 and SCO4451 and SCO4641 ","col1":"LCTSt","col2":"h[e] + lcts[e] -> h[c] + lcts[c] ","col3":"proton symport","col4":false,"col5":0,"col6":1000,"col7":"KEGG ","col8":"Membrane transport","col9":" Lactose transport via proton symport"},{"Unnamed: 0":9,"col0":"SCO6384","col1":"THMmfs","col2":"thm[e] + h[e] <=> thm[c] + h[c]","col3":"proton symport","col4":true,"col5":-1000,"col6":1000,"col7":"KEGG ","col8":"Membrane transport","col9":" Thiamine transport via proton symport"},{"Unnamed: 0":10,"col0":"SCO0290 and SCO7165","col1":"RAFFINabc ","col2":"h2o[c] + atp[c] + raffin[e] -> h[c] + raffin[c] + adp[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Membrane transport","col9":" Raffinose transport via ABC system"},{"Unnamed: 0":11,"col0":"(SCO0290 and SCO7165) or SCO1707","col1":"MELIBabc","col2":"h2o[c] + atp[c] + melib[e] -> h[c] + melib[c] + adp[c] + pi[c] ","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Membrane transport","col9":" Melibiose transport via ABC system"},{"Unnamed: 0":12,"col0":"SCO1344 or SCO1655 or SCO6644 or SCO7563 ","col1":"GTHOXabc","col2":"gthox[e] + h2o[c] + atp[c] -> gthox[c] + h[c] + adp[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"TransportDB","col8":"Membrane transport","col9":" Oxidized glutathione "},{"Unnamed: 0":13,"col0":"SCO1840 or SCO6047 or SCO2677 or SCO6512 or SCO6742 or SCO5009","col1":"HEMabc","col2":"hemeO[e] + h2o[c] + atp[c] -> hemeO[c] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Membrane transport","col9":"Heme transport via ABC system"},{"Unnamed: 0":14,"col0":"SCO1922 or SCO1924 or SCO1925 ","col1":"SUFBCDabc","col2":"sufbcd_2fe2s2[e] + atp[c] + h2o[c] -> sufbcd_2fe2s2[c] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG and TransportDB ","col8":"Membrane transport","col9":"ABC system transport of SufBCD with two bound [2Fe-2S] clusters"},{"Unnamed: 0":15,"col0":"SCO1922 or SCO1924 or SCO1925 ","col1":"SUFBCD4abc","col2":"sufbcd_4fe4s[e] + atp[c] + h2o[c] -> sufbcd_4fe4s[c] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG and TransportDB","col8":"Membrane transport","col9":"ABC system transport of SufBCD with bound [4Fe-4S] cluster"},{"Unnamed: 0":16,"col0":"(SCO2324 or SCO5958 or SCO5959 or SCO5960 or SCO6720 ) and SCO2323 and SCO2325","col1":"COBALTabc","col2":"cobalt2[e] + atp[c] + h2o[c] -> cobalt2[c] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG and transportDB","col8":"Inorganic Ion Transport and metabolism","col9":"Cobalt transport via ABC system"},{"Unnamed: 0":17,"col0":"SCO0636 or SCO2463 or SCO2763 or SCO6683 or SCO6684 or SCO6295","col1":"ACTabc","col2":"act[c] + h2o[c] + atp[c] -> act[e] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Membrane transport","col9":"Actinorhodin transport via ABC system"},{"Unnamed: 0":18,"col0":"SCO4475","col1":"FICYTabc","col2":"h2o[c] + atp[c] + ficytc[c] -> h[c] + ficytc[e] + adp[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG and 100% identity on protein homology with 5 Streptomyces strains (e.g. S. lividans TK24, S. sp. 2114.2,...)","col8":"Membrane transport","col9":"Ferricytochrome transport via ABC system"},{"Unnamed: 0":19,"col0":"SCO5598","col1":"SERabc","col2":"ser__L[e] + h2o[c] + atp[c] -> ser__L[c] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG","col8":"Membrane transport","col9":" Serine transport via ABC system"},{"Unnamed: 0":20,"col0":"SCO2128","col1":"ASO3abc","col2":"aso3[e] + h2o[c] + atp[c] -> aso3[c] + adp[c] + h[c] + pi[c]","col3":"ABC","col4":false,"col5":0,"col6":1000,"col7":"KEGG and 100% identity on protein homology with e.g. S.sp. 2221.1, S. sp. 2114.2, S.sp. 19","col8":"Membrane transport","col9":"Arsenite influx via ABC system"},{"Unnamed: 0":21,"col0":"SCO0295","col1":"MMYAt","col2":"mmyA[e]+ h[c] <=> h[c]+ mmyA[c]","col3":"proton antiport","col4":true,"col5":-1000,"col6":1000,"col7":"KEGG and 90% identity on protein homology with e.g. S. sp. 2114.2, S.sp. 2221.1, S. sp. 19","col8":"Membrane transport","col9":"Methylenomycin A transport via proton antiport "},{"Unnamed: 0":22,"col0":"SCO0295","col1":"MMYBt","col2":"mmyB[e]+ h[c] <=> h[c]+ mmyB[c]","col3":"proton antiport","col4":true,"col5":-1000,"col6":1000,"col7":"KEGG and 90% identity on protein homology with e.g. S. sp. 2114.2, S.sp. 2221.1, S. sp. 19","col8":"Membrane transport","col9":"Methylenomycin B transport via proton antiport "},{"Unnamed: 0":23,"col0":"SCO0242 or SCO6122","col1":"CATECHOLt","col2":"catechol[e] <=> catechol[c] ","col3":"faccilitated diffusion","col4":true,"col5":-1000,"col6":1000,"col7":"100% identity on protein homology with 4 Streptomyces species (e.g. S. sp. 2114.2, S.sp.2221.1, S.sp.111WW2, S.sp. 19)","col8":"Membrane transport","col9":"Catechol transport via facilitated diffusion"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "stir-fried noodle": "Japan"}, {"Prop0": "Prop2", "stir-fried noodle": "Namie"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","stir-fried noodle":"Japan"},{"Prop0":"Prop2","stir-fried noodle":"Namie"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "food prepared and served quickly": "Nether1ands"}, {"Prop0": "Prop2", "food prepared and served quickly": "2003-01-01T00:00:00Z"}, {"Prop0": "Prop3", "food prepared and served quickly": "Dutch cuisine"}, {"Prop0": "Prop4", "food prepared and served quickly": "salad, meat, French fries, Gouda"}, {"Prop0": "Prop5", "food prepared and served quickly": "haircutting establishment"}, {"Prop0": "Prop6", "food prepared and served quickly": "Kapsalon (food)"}, {"Prop0": "Prop7", "food prepared and served quickly": "haircutting establishment"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","food prepared and served quickly":"Netherlands"},{"Prop0":"Prop2","food prepared and served quickly":"2003-01-01T00:00:00Z"},{"Prop0":"Prop3","food prepared and served quickly":"Dutch cuisine"},{"Prop0":"Prop4","food prepared and served quickly":"salad, meat, French fries, Gouda"},{"Prop0":"Prop5","food prepared and served quickly":"haircutting establishment"},{"Prop0":"Prop6","food prepared and served quickly":"Kapsalon (food)"},{"Prop0":"Prop7","food prepared and served quickly":"haircutting establishment"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Auditorio Municipal", "col1": 2500}, {"col0": "Palasport di San Siro", "col1": 18000}, {"col0": "PalaLido", "col1": 5420}, {"col0": "PalaYamamay", "col1": 4490}, {"col0": "Halle Georges-Carpentier", "col1": 5009}, {"col0": "T-Mobile Center", "col1": 19252}, {"col0": "Spyros Kyprianou Athletic Center", "col1": 6255}, {"col0": "McCamish Pavilion", "col1": "&G()()"}, {"col0": "Estadio Luna Park", "col1": 9200}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Auditorio Municipal","col1":2500},{"col0":"Palasport di San Siro","col1":18000},{"col0":"PalaLido","col1":5420},{"col0":"PalaYamamay","col1":4490},{"col0":"Halle Georges-Carpentier","col1":5009},{"col0":"T-Mobile Center","col1":19252},{"col0":"Spyros Kyprianou Athletic Center","col1":6255},{"col0":"McCamish Pavilion","col1":8600},{"col0":"Estadio Luna Park","col1":9200}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "brewed beverage made from seeds of Coffea genus": "Category:Canned coffee"}, {"Prop0": "Prop2", "brewed beverage made from seeds of Coffea genus": "1965-01-01T00:00:00Z"}, {"Prop0": "Prop3", "brewed beverage made from seeds of Coffea genus": "Canned coffee"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","brewed beverage made from seeds of Coffea genus":"Category:Canned coffee"},{"Prop0":"Prop2","brewed beverage made from seeds of Coffea genus":"1965-01-01T00:00:00Z"},{"Prop0":"Prop3","brewed beverage made from seeds of Coffea genus":"Canned coffee"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Bengali cuisine"}, {"Prop0": "Prop2", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "table salt"}, {"Prop0": "Prop3", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"table salt"},{"Prop0":"Prop3","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Chrysler Cirrus", "assemblyPlace": "Sterling Heights, Michigan", "year": 1996, "sold": 46355}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Chrysler Cirrus", "assemblyPlace": "Sterling Heights, Michigan", "year": 1997, "sold": 70549}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Chrysler Cirrus", "assemblyPlace": "5ter1ing Heights, Michigan", "year": 1998, "sold": 66620}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Chrysler Cirrus", "assemblyPlace": "Sterling Heights, Michigan", "year": 1999, "sold": 47911}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Ghrys1er Girrus", "assemblyPlace": "Sterling Heights, Michigan", "year": 2000, "sold": 2030}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Pontiac Aztek", "assemblyPlace": "Ramos Arizpe", "year": 2000, "sold": 11201}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Pontiac Aztek", "assemblyPlace": "Ramos Arizpe", "year": 2001, "sold": 27322}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "8uick KendezVous", "assemblyPlace": "Ramos Arizpe", "year": 2001, "sold": 31754}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Buick Rendezvous", "assemblyPlace": "Ramos Arizpe", "year": 2002, "sold": 61468}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Pontiac Aztek", "assemblyPlace": "Ramos Arizpe", "year": 2002, "sold": 27793}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Pontiac Aztek", "assemblyPlace": "Ramos Arizpe", "year": 2003, "sold": 27354}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Buick Rendezvous", "assemblyPlace": "Ramos Arizpe", "year": 2003, "sold": "7ZG43"}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Graz", "year": 2003, "sold": 55683}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Pekan District", "year": 2003, "sold": 55683}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Tehran", "year": "Z()()3", "sold": 55683}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "8ogor", "year": 2003, "sold": 55683}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "6th of October City", "year": 2003, "sold": 55683}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Johor Bahru", "year": 2003, "sold": 55683}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Santiago Tianguistenco", "year": 2003, "sold": 55683}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Kuala Lumpur", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Jakarta", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Tehran", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Buenos Aires", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Ryton-on-Dunsmore", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Los Andes, Chile", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Barra de Carrasco", "year": 2004, "sold": 795000}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 307", "assemblyPlace": "El Palomar, Buenos Aires", "year": 2004, "sold": 583700}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 307", "assemblyPlace": "Villa Bosch", "year": 2004, "sold": 583700}, {"manufacturer": "Eord Motor Gompany", "manufacturerCountry": "United States", "automobile": "Eord EiVe Hundred", "assemblyPlace": "Chicago", "year": 2004, "sold": 14106}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Pontiac Aztek", "assemblyPlace": "Ramos Arizpe", "year": 2004, "sold": 20588}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Buick Rendezvous", "assemblyPlace": "Ramos Arizpe", "year": 2004, "sold": 60039}, {"manufacturer": "Cadillac", "manufacturerCountry": "United States", "automobile": "Cadillac STS", "assemblyPlace": "Kaliningrad", "year": 2004, "sold": 9484}, {"manufacturer": "Cadillac", "manufacturerCountry": "United States", "automobile": "Cadillac STS", "assemblyPlace": "Kaliningrad Oblast", "year": 2004, "sold": 9484}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Tehran", "year": 2004, "sold": 58954}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Pekan District", "year": 2004, "sold": 58954}, {"manufacturer": "Daim1er AC", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Graz", "year": 2004, "sold": 58954}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "6th of October City", "year": 2004, "sold": 58954}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Bogor", "year": 2004, "sold": 58954}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Johor 8ahru", "year": 2004, "sold": 58954}, {"manufacturer": "Daimler AG", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz E-Class (W211)", "assemblyPlace": "Santiago Tianguistenco", "year": 2004, "sold": 58954}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot Z()G", "assemblyPlace": "Jakarta", "year": 2005, "sold": 676500}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Kuala Lumpur", "year": "Z()()S", "sold": 676500}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Tehran", "year": 2005, "sold": 676500}, {"manufacturer": "Peugeot", "manufacturerCountry": "Erance", "automobile": "Peugeot 206", "assemblyPlace": "Buenos Aires", "year": 2005, "sold": 676500}, {"manufacturer": "Mercedes-Benz", "manufacturerCountry": "Germany", "automobile": "Mercedes-Benz R-Class", "assemblyPlace": "Vance, Alabama", "year": 2005, "sold": 4959}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot |()7", "assemblyPlace": "Kol\u00edn", "year": 2005, "sold": 34600}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Ryton-on-Dunsmore", "year": 2005, "sold": 676500}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Los Andes, Ghi1e", "year": 2005, "sold": 676500}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 307", "assemblyPlace": "Vi11a 8osch", "year": 2005, "sold": 520400}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 206", "assemblyPlace": "Barra de Carrasco", "year": 2005, "sold": 676500}, {"manufacturer": "Peugeot", "manufacturerCountry": "France", "automobile": "Peugeot 307", "assemblyPlace": "El Palomar, Buenos Aires", "year": 2005, "sold": 520400}, {"manufacturer": "Ford Motor Company", "manufacturerCountry": "United States", "automobile": "Ford Five Hundred", "assemblyPlace": "Chicago", "year": 2005, "sold": 107932}, {"manufacturer": "General Motors", "manufacturerCountry": "United States", "automobile": "Buick Rendezvous", "assemblyPlace": "Ramos Arizpe", "year": "Z()()S", "sold": 60589}, {"manufacturer": "General Motors", "manufacturerCountry": "United 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|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "RRQN0", "col1": "sandalwood", "col2": 80209, "col3": "extracted from seeds, or less often, from other parts of fruits"}, {"col0": "RRQN1", "col1": "pistachio nut", "col2": 80025, "col3": "?"}, {"col0": "RRQN2", "col1": "coconut oil", "col2": 34340, "col3": "?"}, {"col0": "KKQN3", "col1": "safflower seed", "col2": 55094, "col3": "?"}, {"col0": "RRQN4", "col1": "avocado", "col2": 59095, "col3": "extracted from seeds, or less often, from other parts of fruits"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"RRQN0","col1":"sandalwood","col2":80209,"col3":"extracted from seeds, or less often, from other parts of fruits"},{"col0":"RRQN1","col1":"pistachio nut","col2":80025,"col3":"?"},{"col0":"RRQN2","col1":"coconut oil","col2":34340,"col3":"?"},{"col0":"RRQN3","col1":"safflower seed","col2":55094,"col3":"?"},{"col0":"RRQN4","col1":"avocado","col2":59095,"col3":"extracted from seeds, or less often, from other parts of fruits"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
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|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
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"col3": null, "col4": null}, {"Unnamed: 0": 15, "col0": "href=\"http://us.rd.yahoo.com/404/*http://www.yahoo.com\">Yahoo!", "col1": "home", "col2": null, "col3": null, "col4": null}, {"Unnamed: 0": 16, "col0": "href=\"http://us.rd.yahoo.com/404/*http://docs.yahoo.com/docs/family/more/\">Yahoo!'s", "col1": null, "col2": null, "col3": null, "col4": null}, {"Unnamed: 0": 17, "col0": "if", "col1": "you", "col2": "try", "col3": "searching", "col4": "below.</p>"}, {"Unnamed: 0": "|&", "col0": "<form", "col1": "name=\"s1\"", "col2": "action=\"http://us.rd.yahoo.com/404/*-http://search.yahoo.com/search\"><fieldset>", "col3": null, "col4": null}, {"Unnamed: 0": 19, "col0": "<legend><label", "col1": "for=\"s1p\">Search", "col2": "the", "col3": "Web</label></legend>", "col4": null}, {"Unnamed: 0": "Z()", "col0": "<input", "col1": "type=\"submit\"", "col2": "value=\"Search\">", "col3": null, "col4": null}, {"Unnamed: 0": 21, "col0": "</fieldset></form>", "col1": null, "col2": null, "col3": null, 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|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "http://fullers.co.uk/rte.asp?id=50"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "Fuller's Brewery"}, {"Prop0": "Prop3", "kind of liquid which is specifically prepared for human consumption": "United Kingdom"}, {"Prop0": "Prop4", "kind of liquid which is specifically prepared for human consumption": "2000-01-01T00:00:00Z"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"http:\/\/fullers.co.uk\/rte.asp?id=50"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"Fuller's Brewery"},{"Prop0":"Prop3","kind of liquid which is specifically prepared for human consumption":"United Kingdom"},{"Prop0":"Prop4","kind of liquid which is specifically prepared for human consumption":"2000-01-01T00:00:00Z"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Stele of Irynefer-MAHG D 0051", "col1": 54.0, "col2": 43.0}, {"col0": "Stele Giustiniani", "col1": 1.43, "col2": null}, {"col0": "Eunerary mea1 of chief treasurer Nefer~8 S|", "col1": 0.95, "col2": 1.11}, {"col0": "King founding a temple-MAHG 27812", "col1": 97.0, "col2": null}, {"col0": "Victory stele-AO 2678", "col1": 34.5, "col2": 28.5}, {"col0": "stele (MND 919)", "col1": 128.0, "col2": 71.0}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Stele of Irynefer-MAHG D 0051","col1":54.0,"col2":43.0},{"col0":"Stele Giustiniani","col1":1.43,"col2":null},{"col0":"Funerary meal of chief treasurer Nefer-B 51","col1":0.95,"col2":1.11},{"col0":"King founding a temple-MAHG 27812","col1":97.0,"col2":null},{"col0":"Victory stele-AO 2678","col1":34.5,"col2":28.5},{"col0":"stele (MND 919)","col1":128.0,"col2":71.0}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "primarily liquid food": "Tripe soup in Greece"}, {"Prop0": "Prop2", "primarily liquid food": "Greece"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","primarily liquid food":"Tripe soup in Greece"},{"Prop0":"Prop2","primarily liquid food":"Greece"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "food, edible in the raw state": "Vitex agnus-castus"}, {"Prop0": "Prop2", "food, edible in the raw state": "433OSF3U8A"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","food, edible in the raw state":"Vitex agnus-castus"},{"Prop0":"Prop2","food, edible in the raw state":"433OSF3U8A"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Green Township", "col1": 24.13, "col2": 265}, {"col0": "Green Township", "col1": 36.03, "col2": 301}, {"col0": "Green Township", "col1": 30.36, "col2": "ZG&"}, {"col0": "Green Township", "col1": 33.25, "col2": 239}, {"col0": "Green Township", "col1": 33.47, "col2": 214}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Green Township","col1":24.13,"col2":265},{"col0":"Green Township","col1":36.03,"col2":301},{"col0":"Green Township","col1":30.36,"col2":268},{"col0":"Green Township","col1":33.25,"col2":239},{"col0":"Green Township","col1":33.47,"col2":214}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Peruvian cuisine"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Pollada peruana"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Peru"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "Peru"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Peruvian cuisine"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Pollada peruana"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Peru"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"Peru"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "NGC 7252", "col1": 12.06, "col2": 0.0157}, {"col0": "NGC 34", "col1": 13.52, "col2": 0.019617}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"NGC 7252","col1":12.06,"col2":0.0157},{"col0":"NGC 34","col1":13.52,"col2":0.019617}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "fruit of Punica granatum": "6.3"}, {"Prop0": "Prop2", "fruit of Punica granatum": "pomegranate"}, {"Prop0": "Prop3", "fruit of Punica granatum": "ark:/S47Z|/SZZ()7dS7~afqG~4f7b~&4&|~S|f|d4|e33fa"}, {"Prop0": "Prop4", "fruit of Punica granatum": "-0450-01-01T00:00:00Z, -0350-01-01T00:00:00Z"}, {"Prop0": "Prop5", "fruit of Punica granatum": "Mus\u00e9e Saint-Raymond"}, {"Prop0": "Prop6", "fruit of Punica granatum": "26197 e"}, {"Prop0": "Prop7", "fruit of Punica granatum": "6.3"}, {"Prop0": "Prop&", "fruit of Punica granatum": "Saint-Raymond Museum reserves"}, {"Prop0": "Prop9", "fruit of Punica granatum": "terracotta"}, {"Prop0": "Prop10", "fruit of Punica granatum": "Charles Othon Fr\u00e9d\u00e9ric Jean-Baptiste de Clarac, Edm\u00e9-Antoine Durand, City of Toulouse"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","fruit of Punica granatum":"6.3"},{"Prop0":"Prop2","fruit of Punica granatum":"pomegranate"},{"Prop0":"Prop3","fruit of Punica granatum":"ark:\/54721\/52207d57-af96-4f7b-8481-51f1d41e33fa"},{"Prop0":"Prop4","fruit of Punica granatum":"-0450-01-01T00:00:00Z, -0350-01-01T00:00:00Z"},{"Prop0":"Prop5","fruit of Punica granatum":"Musée Saint-Raymond"},{"Prop0":"Prop6","fruit of Punica granatum":"26197 e"},{"Prop0":"Prop7","fruit of Punica granatum":"6.3"},{"Prop0":"Prop8","fruit of Punica granatum":"Saint-Raymond Museum reserves"},{"Prop0":"Prop9","fruit of Punica granatum":"terracotta"},{"Prop0":"Prop10","fruit of Punica granatum":"Charles Othon Frédéric Jean-Baptiste de Clarac, Edmé-Antoine Durand, City of Toulouse"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "culinary technique determining the final shape and size of cut pieces of food": "rolling, cutting"}, {"Prop0": "Prop2", "culinary technique determining the final shape and size of cut pieces of food": "ribbon"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","culinary technique determining the final shape and size of cut pieces of food":"rolling, cutting"},{"Prop0":"Prop2","culinary technique determining the final shape and size of cut pieces of food":"ribbon"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Rome", "col1": 34950}, {"col0": "Cortland", "col1": 17556}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Rome","col1":34950},{"col0":"Cortland","col1":17556}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "Indonesia, Singapore, Malaysia"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "saenggang~cha"}, {"Prop0": "Prop3", "kind of liquid which is specifically prepared for human consumption": "saenggang-ch'a"}, {"Prop0": "Prop4", "kind of liquid which is specifically prepared for human consumption": "tea, sucrose, ginger"}, {"Prop0": "Prop5", "kind of liquid which is specifically prepared for human consumption": "Zingiber officinale"}, {"Prop0": "Prop6", "kind of liquid which is specifically prepared for human consumption": "Ginger tea"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Indonesia, Singapore, Malaysia"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"saenggang-cha"},{"Prop0":"Prop3","kind of liquid which is specifically prepared for human consumption":"saenggang-ch'a"},{"Prop0":"Prop4","kind of liquid which is specifically prepared for human consumption":"tea, sucrose, ginger"},{"Prop0":"Prop5","kind of liquid which is specifically prepared for human consumption":"Zingiber officinale"},{"Prop0":"Prop6","kind of liquid which is specifically prepared for human consumption":"Ginger tea"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Giolou", "col1": "Stavros Vrachnou", "col2": "Paphos District", "col3": 762}, {"col0": "Akhelia", "col1": "Sergis Thrasivoulou", "col2": "Paphos District", "col3": 145}, {"col0": "Arminou", "col1": "Andreas Petrou", "col2": "Paphos District", "col3": 24}, {"col0": "Argaka", "col1": "5piros Pe1opida", "col2": "Paphos District", "col3": 1078}, {"col0": "Anadhiou", "col1": "Savvas Chatzisavvas", "col2": "Paphos District", "col3": 17}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Giolou","col1":"Stavros Vrachnou","col2":"Paphos District","col3":762},{"col0":"Akhelia","col1":"Sergis Thrasivoulou","col2":"Paphos District","col3":145},{"col0":"Arminou","col1":"Andreas Petrou","col2":"Paphos District","col3":24},{"col0":"Argaka","col1":"Spiros Pelopida","col2":"Paphos District","col3":1078},{"col0":"Anadhiou","col1":"Savvas Chatzisavvas","col2":"Paphos District","col3":17}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Special Operation Group", "col1": 1370}, {"col0": "Long Beach Police Department", "col1": 1164}, {"col0": "Wayne County Sheriff's Office", "col1": 170}, {"col0": "Punjab Police", "col1": 180000}, {"col0": "Punjab Police", "col1": 70000}, {"col0": "Lee County Sheriff's Office", "col1": "4Z"}, {"col0": "State Police", "col1": 8167}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Special Operation Group","col1":1370},{"col0":"Long Beach Police Department","col1":1164},{"col0":"Wayne County Sheriff's Office","col1":170},{"col0":"Punjab Police","col1":180000},{"col0":"Punjab Police","col1":70000},{"col0":"Lee County Sheriff's Office","col1":42},{"col0":"State Police","col1":8167}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "drink containing alcohols, typically ethanol": "Tejuino"}, {"Prop0": "Prop2", "drink containing alcohols, typically ethanol": "Mexico"}, {"Prop0": "Prop3", "drink containing alcohols, typically ethanol": "Mexico"}, {"Prop0": "Prop4", "drink containing alcohols, typically ethanol": "maize"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","drink containing alcohols, typically ethanol":"Tejuino"},{"Prop0":"Prop2","drink containing alcohols, typically ethanol":"Mexico"},{"Prop0":"Prop3","drink containing alcohols, typically ethanol":"Mexico"},{"Prop0":"Prop4","drink containing alcohols, typically ethanol":"maize"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
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|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Walferdange", "col1": 8418, "col2": 228}, {"col0": "Wiltz", "col1": 7533, "col2": 477}, {"col0": "Kiischpelt", "col1": 1209, "col2": 295}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Walferdange","col1":8418,"col2":228},{"col0":"Wiltz","col1":7533,"col2":477},{"col0":"Kiischpelt","col1":1209,"col2":295}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Wegestein", "col1": "Panschwitz-Kuckau", "col2": "cultural heritage monument in Germany"}, {"col0": "Wegweiser", "col1": "Weiden in der Oberpfalz", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "Eberfing", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "Fberfing", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "Pyrbaum", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "Pyrbaum", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "0berhaching", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "Oberhaching", "col2": "architectural heritage monument in Bavaria"}, {"col0": "Wegweiser", "col1": "Oberhaching", "col2": "architectural heritage monument in Bavaria"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Wegestein","col1":"Panschwitz-Kuckau","col2":"cultural heritage monument in Germany"},{"col0":"Wegweiser","col1":"Weiden in der Oberpfalz","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Eberfing","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Eberfing","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Pyrbaum","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Pyrbaum","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Oberhaching","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Oberhaching","col2":"architectural heritage monument in Bavaria"},{"col0":"Wegweiser","col1":"Oberhaching","col2":"architectural heritage monument in Bavaria"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "QJFV0", "col1": "India", "col2": "Bengali cuisine", "col3": "food prepared from mushrooms"}, {"col0": "QJFV1", "col1": "India", "col2": "Bengali cuisine", "col3": "?"}, {"col0": "QJFV2", "col1": "India", "col2": "Bengali cuisine", "col3": "?"}, {"col0": "QJFV3", "col1": "India", "col2": "Bengali cuisine", "col3": "food prepared from mushrooms"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"QJFV0","col1":"India","col2":"Bengali cuisine","col3":"food prepared from mushrooms"},{"col0":"QJFV1","col1":"India","col2":"Bengali cuisine","col3":"?"},{"col0":"QJFV2","col1":"India","col2":"Bengali cuisine","col3":"?"},{"col0":"QJFV3","col1":"India","col2":"Bengali cuisine","col3":"food prepared from mushrooms"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "animal flesh eaten as food": "Lama glama"}, {"Prop0": "Prop2", "animal flesh eaten as food": "44M28X73NY"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","animal flesh eaten as food":"Lama glama"},{"Prop0":"Prop2","animal flesh eaten as food":"44M28X73NY"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Beauty and the Beast", "col1": 84, "col2": 424967620, "col3": 25000000}, {"col0": "Monsters vs. Aliens", "col1": 94, "col2": 381509870, "col3": 175000000}, {"col0": "The Eo* and the Hound", "col1": 83, "col2": 63456988, "col3": 12000000}, {"col0": "Despicable Me", "col1": 95, "col2": 543113985, "col3": 69000000}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Beauty and the Beast","col1":84,"col2":424967620,"col3":25000000},{"col0":"Monsters vs. Aliens","col1":94,"col2":381509870,"col3":175000000},{"col0":"The Fox and the Hound","col1":83,"col2":63456988,"col3":12000000},{"col0":"Despicable Me","col1":95,"col2":543113985,"col3":69000000}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Ischia", "col1": 9.9, "col2": "Phlegraean Islands", "col3": 46.3}, {"col0": "Aneityum", "col1": null, "col2": "New Hebrides", "col3": 159.2}, {"col0": "Nakanoshima", "col1": "q.()", "col2": "Tokara Islands", "col3": 34.47}, {"col0": "Rarotonga", "col1": 11.0, "col2": "Cook Islands", "col3": 67.1}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Ischia","col1":9.9,"col2":"Phlegraean Islands","col3":46.3},{"col0":"Aneityum","col1":null,"col2":"New Hebrides","col3":159.2},{"col0":"Nakanoshima","col1":9.0,"col2":"Tokara Islands","col3":34.47},{"col0":"Rarotonga","col1":11.0,"col2":"Cook Islands","col3":67.1}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "graph embedding", "col1": "natural language processing", "col2": null, "col3": "embedding"}, {"col0": "modulation", "col1": "information transfer", "col2": "physical layer", "col3": "telecommunication"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"graph embedding","col1":"natural language processing","col2":null,"col3":"embedding"},{"col0":"modulation","col1":"information transfer","col2":"physical layer","col3":"telecommunication"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Janikhel District", "col1": 2086}, {"col0": "Yawan", "col1": 2164}, {"col0": "Kahmard", "col1": 1475}, {"col0": "Kamdesh", "col1": 2536}, {"col0": "Mir 8acha Rot", "col1": 1702}, {"col0": "Qurghan", "col1": 285}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Janikhel District","col1":2086},{"col0":"Yawan","col1":2164},{"col0":"Kahmard","col1":1475},{"col0":"Kamdesh","col1":2536},{"col0":"Mir Bacha Kot","col1":1702},{"col0":"Qurghan","col1":285}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Puente Atalaya, C\u00facuta", "col1": 18.0}, {"col0": "Passeres sobre el Riu Ripoll", "col1": 2.0}, {"col0": "516 Arouca", "col1": 1.2}, {"col0": "Buluowan Bridge", "col1": 2.5}, {"col0": "Babolsar Cable Bridge", "col1": 5.6}, {"col0": "Runyang Yangtze River South Bridge", "col1": 39.2}, {"col0": "Xiushan Bridge", "col1": "Z4.()"}, {"col0": "Jijiang Yangtze Bridge", "col1": 32.5}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Puente Atalaya, Cúcuta","col1":18.0},{"col0":"Passeres sobre el Riu Ripoll","col1":2.0},{"col0":"516 Arouca","col1":1.2},{"col0":"Buluowan Bridge","col1":2.5},{"col0":"Babolsar Cable Bridge","col1":5.6},{"col0":"Runyang Yangtze River South Bridge","col1":39.2},{"col0":"Xiushan Bridge","col1":24.0},{"col0":"Jijiang Yangtze Bridge","col1":32.5}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Willersdorf", "col1": 333, "col2": 7.0101, "col3": 394}, {"col0": "8iescas", "col1": 1584, "col2": 189.092452, "col3": 860}, {"col0": "Wallendorf", "col1": 372, "col2": 3.47, "col3": 231}, {"col0": "Karl", "col1": 212, "col2": 6.55, "col3": 385}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Willersdorf","col1":333,"col2":7.0101,"col3":394},{"col0":"Biescas","col1":1584,"col2":189.092452,"col3":860},{"col0":"Wallendorf","col1":372,"col2":3.47,"col3":231},{"col0":"Karl","col1":212,"col2":6.55,"col3":385}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Met Museum artworks on display"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Metropolitan Museum of Art, The American Wing"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Met Museum artworks on display"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Metropolitan Museum of Art, The American Wing"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Estonia"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Estonia"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "rye bread, sprat"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Estonia"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Estonia"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"rye bread, sprat"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "PGC 50584", "col1": 5601.0}, {"col0": "ESO 54-12", "col1": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"PGC 50584","col1":5601.0},{"col0":"ESO 54-12","col1":null}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "Bengali cuisine"}, {"Prop0": "Prop2", "dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"Bengali cuisine"},{"Prop0":"Prop2","dishes and prepared foods primarily featuring plants or parts of plants culinarily used as vegetables":"India"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "V851 Cen", "col1": -61.3664354962, "col2": 74.0571, "col3": -0.13}, {"col0": "HD |GSSq()", "col1": 21.4459415029, "col2": 32.579, "col3": 7.29}, {"col0": "BM CVn", "col1": 38.8804217081, "col2": 119.5243, "col3": -0.01}, {"col0": "IL Hya", "col1": -23.8263093383, "col2": 105.126, "col3": -0.43}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"V851 Cen","col1":-61.3664354962,"col2":74.0571,"col3":-0.13},{"col0":"HD 165590","col1":21.4459415029,"col2":32.579,"col3":7.29},{"col0":"BM CVn","col1":38.8804217081,"col2":119.5243,"col3":-0.01},{"col0":"IL Hya","col1":-23.8263093383,"col2":105.126,"col3":-0.43}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "NGID0", "col1": "1LIHG", "col2": 500383, "col3": "food, edible in the raw state"}, {"col0": "NC1D|", "col1": "PHYIX", "col2": "S()4374", "col3": "?"}, {"col0": "NGID2", "col1": "NEELA", "col2": 506073, "col3": "?"}, {"col0": "NGID3", "col1": "1CIDG", "col2": 28882, "col3": "?"}, {"col0": "NGID4", "col1": "1ANHG", "col2": 42334, "col3": "food, edible in the raw state"}, {"col0": "NGID5", "col1": "1RIBG", "col2": 24448, "col3": "food, edible in the raw state"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"NGID0","col1":"1LIHG","col2":500383,"col3":"food, edible in the raw state"},{"col0":"NGID1","col1":"PHYIX","col2":504374,"col3":"?"},{"col0":"NGID2","col1":"NEELA","col2":506073,"col3":"?"},{"col0":"NGID3","col1":"1CIDG","col2":28882,"col3":"?"},{"col0":"NGID4","col1":"1ANHG","col2":42334,"col3":"food, edible in the raw state"},{"col0":"NGID5","col1":"1RIBG","col2":24448,"col3":"food, edible in the raw state"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Phra Samut Chedi", "col1": 128200}, {"col0": "Wang Chin", "col1": 46124}, {"col0": "Manang", "col1": 17596}, {"col0": "Nong Yai", "col1": 23625}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Phra Samut Chedi","col1":128200},{"col0":"Wang Chin","col1":46124},{"col0":"Manang","col1":17596},{"col0":"Nong Yai","col1":23625}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "AAlabama", "col1": "A11abama"}, {"col0": "A1abbama", "col1": "Alabamma"}, {"col0": "A1askaa", "col1": "Alsaka"}, {"col0": "Arrizona", "col1": "Arizonia"}, {"col0": "Arizona", "col1": "Arzinoa"}, {"col0": "Californiaa", "col1": "Califronia"}, {"col0": "Coloradoo", "col1": "Ga1orado"}, {"col0": "Coloradoo", "col1": "Colarado"}, {"col0": "Connecticut", "col1": "connecticutt"}, {"col0": "Connecticutt", "col1": "conneticutt"}, {"col0": "Connecticutt", "col1": "connetticut"}, {"col0": "Connecticut", "col1": "connetticutt"}, {"col0": "Connecticutt", "col1": "conneticut"}, {"col0": "De1aWare", "col1": "Delawere"}, {"col0": "E1oridaa", "col1": "Floridas"}, {"col0": "Florida", "col1": "Floryda"}, {"col0": "Fllorida", "col1": "Florda"}, {"col0": "Floridaa", "col1": "Flordia"}, {"col0": "GGeorgia", "col1": "Georgeia"}, {"col0": "Georgiaa", "col1": "Georgea"}, {"col0": "Hawaiii", "col1": "Hawai"}, {"col0": "Hawaiii", "col1": "Howaii"}, {"col0": "Hawaii", "col1": "Hawii"}, {"col0": "Hawaiii", "col1": "Hawaai"}, {"col0": "11daho", "col1": "Idahoe"}, {"col0": "Idahoo", "col1": "1deho"}, {"col0": "111inoiss", "col1": "Illanoise"}, {"col0": "Iowwa", "col1": "Iowha"}, {"col0": "Iowa", "col1": "Ioaw"}, {"col0": "Iowaa", "col1": "Iwoa"}, {"col0": "Kentuckyy", "col1": "kentuky"}, {"col0": "Kentucky", "col1": "kentucy"}, {"col0": "Louisianaa", "col1": "Louiseiana"}, {"col0": "Maine", "col1": "main"}, {"col0": "Maine", "col1": "miane"}, {"col0": "Maryland", "col1": "mary land"}, {"col0": "Marylandd", "col1": "marryland"}, {"col0": "Massachusetts", "col1": "Massachussetts"}, {"col0": "Massachusettss", "col1": "Masachusetts"}, {"col0": "Massachusetts", "col1": "Masachusets"}, {"col0": "Massachusetts", "col1": "Massachussets"}, {"col0": "Micchigan", "col1": "michgan"}, {"col0": "Michigan", "col1": "michagin"}, {"col0": "Michhigan", "col1": "michagan"}, {"col0": "Minnesota", "col1": "Minesota"}, {"col0": "Mississippii", "col1": "Mississipi"}, {"col0": "Mississsippi", "col1": "Misouri"}, {"col0": "Mississiippi", "col1": "Missisipi"}, {"col0": "Misssissippi", "col1": "Missoury"}, {"col0": "Mississippi", "col1": "Missisippi"}, {"col0": "Nebraskka", "col1": "Nebrascka"}, {"col0": "Nevada", "col1": "Nevadaa"}, {"col0": "Nevvada", "col1": "Newvada"}, {"col0": "Nevaada", "col1": "NaVada"}, {"col0": "New Haampshire", "col1": "NewHampshire"}, {"col0": "New Jersey", "col1": "newjersey"}, {"col0": "New MMexico", "col1": "NewMexico"}, {"col0": "New Yorkk", "col1": "Newyork"}, {"col0": "North Garo1inaa", "col1": "North Caroleina"}, {"col0": "North Dakota", "col1": "n.dak"}, {"col0": "North Dakota", "col1": "northdakota"}, {"col0": "North Dakota", "col1": "ndak"}, {"col0": "Ohio", "col1": "Oiho"}, {"col0": "Oklahoma", "col1": "Okalahoma"}, {"col0": "Oregonn", "col1": "Orgon"}, {"col0": "Pennsylvania", "col1": "Pensylvania"}, {"col0": "Rhode Island", "col1": "road island"}, {"col0": "Rhode Islandd", "col1": "rhodeisland"}, {"col0": "Rhode Island", "col1": "rode island"}, {"col0": "Rhode Islandd", "col1": "rhod island"}, {"col0": "South Carolina", "col1": "southcarolina"}, {"col0": "South Carollina", "col1": "sout carolin"}, {"col0": "South Carolinaa", "col1": "south carolna"}, {"col0": "5outh Garo1inaa", "col1": "souh carolia"}, {"col0": "South Carolinaa", "col1": "south carolina"}, {"col0": "South Dakotaa", "col1": "southdakota"}, {"col0": "Tennessee", "col1": "Tennesee"}, {"col0": "Tennessee", "col1": "Tennisse"}, {"col0": "Tennessee", "col1": "Tennissee"}, {"col0": "Tennessee", "col1": "Tennisee"}, {"col0": "Texass", "col1": "texes"}, {"col0": "Texas", "col1": "texis"}, {"col0": "Utahh", "col1": "Utes"}, {"col0": "Utahh", "col1": "Utar"}, {"col0": "West Virginia", "col1": "West Virgina"}, {"col0": "West Virginiia", "col1": "West Virgnia"}, {"col0": "Wyoming", "col1": "Whyoming"}, {"col0": "Wyyoming", "col1": "Wioming"}, {"col0": "Wyomingg", "col1": "Wisconson"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"AAlabama","col1":"Allabama"},{"col0":"Alabbama","col1":"Alabamma"},{"col0":"Alaskaa","col1":"Alsaka"},{"col0":"Arrizona","col1":"Arizonia"},{"col0":"Arizona","col1":"Arzinoa"},{"col0":"Californiaa","col1":"Califronia"},{"col0":"Coloradoo","col1":"Calorado"},{"col0":"Coloradoo","col1":"Colarado"},{"col0":"Connecticut","col1":"connecticutt"},{"col0":"Connecticutt","col1":"conneticutt"},{"col0":"Connecticutt","col1":"connetticut"},{"col0":"Connecticut","col1":"connetticutt"},{"col0":"Connecticutt","col1":"conneticut"},{"col0":"Delaware","col1":"Delawere"},{"col0":"Floridaa","col1":"Floridas"},{"col0":"Florida","col1":"Floryda"},{"col0":"Fllorida","col1":"Florda"},{"col0":"Floridaa","col1":"Flordia"},{"col0":"GGeorgia","col1":"Georgeia"},{"col0":"Georgiaa","col1":"Georgea"},{"col0":"Hawaiii","col1":"Hawai"},{"col0":"Hawaiii","col1":"Howaii"},{"col0":"Hawaii","col1":"Hawii"},{"col0":"Hawaiii","col1":"Hawaai"},{"col0":"IIdaho","col1":"Idahoe"},{"col0":"Idahoo","col1":"Ideho"},{"col0":"Illinoiss","col1":"Illanoise"},{"col0":"Iowwa","col1":"Iowha"},{"col0":"Iowa","col1":"Ioaw"},{"col0":"Iowaa","col1":"Iwoa"},{"col0":"Kentuckyy","col1":"kentuky"},{"col0":"Kentucky","col1":"kentucy"},{"col0":"Louisianaa","col1":"Louiseiana"},{"col0":"Maine","col1":"main"},{"col0":"Maine","col1":"miane"},{"col0":"Maryland","col1":"mary land"},{"col0":"Marylandd","col1":"marryland"},{"col0":"Massachusetts","col1":"Massachussetts"},{"col0":"Massachusettss","col1":"Masachusetts"},{"col0":"Massachusetts","col1":"Masachusets"},{"col0":"Massachusetts","col1":"Massachussets"},{"col0":"Micchigan","col1":"michgan"},{"col0":"Michigan","col1":"michagin"},{"col0":"Michhigan","col1":"michagan"},{"col0":"Minnesota","col1":"Minesota"},{"col0":"Mississippii","col1":"Mississipi"},{"col0":"Mississsippi","col1":"Misouri"},{"col0":"Mississiippi","col1":"Missisipi"},{"col0":"Misssissippi","col1":"Missoury"},{"col0":"Mississippi","col1":"Missisippi"},{"col0":"Nebraskka","col1":"Nebrascka"},{"col0":"Nevada","col1":"Nevadaa"},{"col0":"Nevvada","col1":"Newvada"},{"col0":"Nevaada","col1":"Navada"},{"col0":"New Haampshire","col1":"NewHampshire"},{"col0":"New Jersey","col1":"newjersey"},{"col0":"New MMexico","col1":"NewMexico"},{"col0":"New Yorkk","col1":"Newyork"},{"col0":"North Carolinaa","col1":"North Caroleina"},{"col0":"North Dakota","col1":"n.dak"},{"col0":"North Dakota","col1":"northdakota"},{"col0":"North Dakota","col1":"ndak"},{"col0":"Ohio","col1":"Oiho"},{"col0":"Oklahoma","col1":"Okalahoma"},{"col0":"Oregonn","col1":"Orgon"},{"col0":"Pennsylvania","col1":"Pensylvania"},{"col0":"Rhode Island","col1":"road island"},{"col0":"Rhode Islandd","col1":"rhodeisland"},{"col0":"Rhode Island","col1":"rode island"},{"col0":"Rhode Islandd","col1":"rhod island"},{"col0":"South Carolina","col1":"southcarolina"},{"col0":"South Carollina","col1":"sout carolin"},{"col0":"South Carolinaa","col1":"south carolna"},{"col0":"South Carolinaa","col1":"souh carolia"},{"col0":"South Carolinaa","col1":"south carolina"},{"col0":"South Dakotaa","col1":"southdakota"},{"col0":"Tennessee","col1":"Tennesee"},{"col0":"Tennessee","col1":"Tennisse"},{"col0":"Tennessee","col1":"Tennissee"},{"col0":"Tennessee","col1":"Tennisee"},{"col0":"Texass","col1":"texes"},{"col0":"Texas","col1":"texis"},{"col0":"Utahh","col1":"Utes"},{"col0":"Utahh","col1":"Utar"},{"col0":"West Virginia","col1":"West Virgina"},{"col0":"West Virginiia","col1":"West Virgnia"},{"col0":"Wyoming","col1":"Whyoming"},{"col0":"Wyyoming","col1":"Wioming"},{"col0":"Wyomingg","col1":"Wisconson"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "type of dish comprised of fish": "Bengali cuisine"}, {"Prop0": "Prop2", "type of dish comprised of fish": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","type of dish comprised of fish":"Bengali cuisine"},{"Prop0":"Prop2","type of dish comprised of fish":"India"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Ayios Ioannis", "col1": "Agios Ioannis, Agios Vasilios Rethimno"}, {"col0": "Episkopi", "col1": "Argos"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Ayios Ioannis","col1":"Agios Ioannis, Agios Vasilios Rethimno"},{"col0":"Episkopi","col1":"Argos"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "2010 Australian National Time Trial Championships (women)", "col1": "2009 Australian National Time Trial Championships (women)"}, {"col0": "2017 Croatia National Road Cycling Championships Men ITT", "col1": "2016 Croatia National Road Cycling Championships Men ITT"}, {"col0": "2022 Australian National Road Race Championships (women)", "col1": "2021 Australian National Road Race Championships (women)"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"2010 Australian National Time Trial Championships (women)","col1":"2009 Australian National Time Trial Championships (women)"},{"col0":"2017 Croatia National Road Cycling Championships Men ITT","col1":"2016 Croatia National Road Cycling Championships Men ITT"},{"col0":"2022 Australian National Road Race Championships (women)","col1":"2021 Australian National Road Race Championships (women)"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Balsamic Roasted Turkey Salad"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "United States of America"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "tomato, Romaine lettuce, turkey meat, basil"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Balsamic Roasted Turkey Salad"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"United States of America"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"tomato, Romaine lettuce, turkey meat, basil"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Unnamed: 0": 0, "col0": 380.5549, "col1": 381.3475, "col2": "Hand", "col3": "FailOnTouchObstacleMed", "col4": "PickedUp"}, {"Unnamed: 0": 1, "col0": 380.7, "col1": 381.2222, "col2": "Hand", "col3": "FailOnTouchObstacleLong", "col4": "RightGate"}, {"Unnamed: 0": 2, "col0": "3&|.Z333", "col1": "3&|.Z444", "col2": "Hand", "col3": "FailOnTouchObstacleLong", "col4": "RightGate"}, {"Unnamed: 0": 3, "col0": 382.4, "col1": 382.9667, "col2": "Hand", "col3": "FailOnTouchObstacleLong", "col4": "Pushed"}, {"Unnamed: 0": 4, "col0": 382.4778, "col1": 382.9222, "col2": "Hand", "col3": "FailOnTouchObstacleShort", "col4": "RightGate"}, {"Unnamed: 0": 5, "col0": 382.4778, "col1": 382.9111, "col2": "Hand", "col3": "Eai10nTouch0bstac1eLong", "col4": "RightGate"}, {"Unnamed: 0": 6, "col0": 383.0455, "col1": 384.0956, "col2": "Hand", "col3": "FailOnTouchObstacleLong", "col4": "PickedUp"}, {"Unnamed: 0": 7, "col0": 384.5334, "col1": 384.7111, "col2": "Hand", "col3": "FailOnTouchObstacleLong", "col4": "RightGate"}, {"Unnamed: 0": 8, "col0": 384.5778, "col1": 384.8111, "col2": "HandToWrist", "col3": "FailOnTouchObstacleLong", "col4": "RightGate"}, {"Unnamed: 0": 9, "col0": 384.6111, "col1": 384.7556, "col2": "Hand", "col3": "FailOnTouchObstacleShort", "col4": "RightGate"}, {"Unnamed: 0": 10, "col0": 384.6333, "col1": 384.8333, "col2": "HandToWrist", "col3": "FailOnTouchObstacleShort", "col4": "RightGate"}, {"Unnamed: 0": 11, "col0": 384.6889, "col1": 389.6667, "col2": "WristToElbow", "col3": "Eai10nTouch0bstac1eLong", "col4": "RightGate"}, {"Unnamed: 0": 12, "col0": 384.7, "col1": 384.9111, "col2": "Hand", "col3": "Eai10nTouch0bstac1eMed", "col4": "LeftGate"}, {"Unnamed: 0": 13, "col0": "3&4.7|||", "col1": 386.3889, "col2": "WristToElbow", "col3": "FailOnTouchObstacleShort", "col4": "RightGate"}, {"Unnamed: 0": 14, "col0": 384.7333, "col1": 385.5222, "col2": "HandToWrist", "col3": "FailOnTouchObstacleMed", "col4": "LeftGate"}, {"Unnamed: 0": 15, "col0": 384.8111, "col1": 389.5556, "col2": "WristToElbow", "col3": "FailOnTouchObstacleMed", "col4": "LeftGate"}, {"Unnamed: 0": 16, "col0": 385.4667, "col1": 387.6889, "col2": "Hand", "col3": "FailOnTouchObstacleLong-1", "col4": "Pushed"}, {"Unnamed: 0": 17, "col0": 385.5445, "col1": 388.3556, "col2": "Hand", "col3": "FailOnTouchObstacleMed-1", "col4": "RightGate"}, {"Unnamed: 0": 18, "col0": 385.5556, "col1": 388.3556, "col2": "Hand", "col3": "FailOnTouchObstacleShort-1", "col4": "LeftGate"}, {"Unnamed: 0": 19, "col0": "3&S.SSSG", "col1": 386.0889, "col2": "Hand", "col3": "FailOnTouchObstacleLong-1", "col4": "LeftGate"}, {"Unnamed: 0": 20, "col0": 385.8604, "col1": 387.48, "col2": "Hand", "col3": "FailOnTouchObstacleLong-1", "col4": "PickedUp"}, {"Unnamed: 0": 21, "col0": "3&S.&GG7", "col1": 385.8889, "col2": "Hand", "col3": "FailOnTouchObstacleLong-1", "col4": "RightGate"}, {"Unnamed: 0": 22, "col0": 385.9111, "col1": 387.6333, "col2": "Hand", "col3": "FailOnTouchObstacleLong-1", "col4": "RightGate"}, {"Unnamed: 0": 23, "col0": 386.4, "col1": 387.9111, "col2": "HandToWrist", "col3": "FailOnTouchObstacleShort-1", "col4": "LeftGate"}, {"Unnamed: 0": 24, "col0": 386.4, "col1": 387.9111, "col2": "HandToWrist", "col3": "FailOnTouchObstacleMed-1", "col4": "RightGate"}, {"Unnamed: 0": 25, "col0": "3&&.()", "col1": 389.6444, "col2": "WristToElbow", "col3": "FailOnTouchObstacleShort", "col4": "RightGate"}, {"Unnamed: 0": 26, "col0": 388.0667, "col1": 388.6556, "col2": "HandToWrist", "col3": "FailOnTouchObstacleMed-1", "col4": "RightGate"}, {"Unnamed: 0": 27, "col0": 388.0667, "col1": 388.5334, "col2": "HandToWrist", "col3": "Eai10nTouch0bstac1e5hort~|", "col4": "LeftGate"}, {"Unnamed: 0": 28, "col0": "3&&.3ZZZ", "col1": 389.3333, "col2": "WristToF1boW", "col3": "FailOnTouchObstacleShort-1", "col4": "LeftGate"}, {"Unnamed: 0": "Zq", "col0": 388.3222, "col1": 389.3445, "col2": "WristToElbow", "col3": "FailOnTouchObstacleMed-1", "col4": "KightCate"}, {"Unnamed: 0": 30, "col0": 388.8333, "col1": 389.1556, "col2": "Hand", "col3": "Target", "col4": "Pushed"}, {"Unnamed: 0": 31, "col0": 389.0439, "col1": null, "col2": "Hand", "col3": "Target", "col4": "PickedUp"}, {"Unnamed: 0": 32, "col0": 389.1778, "col1": 389.4667, "col2": "HandToWrist", "col3": "FailOnTouchObstacleMed-1", "col4": "RightGate"}, {"Unnamed: 0": 33, "col0": 389.3111, "col1": 389.4778, "col2": "Hand", "col3": "Eai10nTouch0bstac1eMed~|", "col4": "RightGate"}, {"Unnamed: 0": "34", "col0": 389.4667, "col1": 389.6222, "col2": "HandToWrist", "col3": "FailOnTouchObstacleMed", "col4": "LeftGate"}, {"Unnamed: 0": 35, "col0": 389.5111, "col1": 389.6333, "col2": "Hand", "col3": "FailOnTouchObstacleMed", "col4": "LeftGate"}, {"Unnamed: 0": 36, "col0": 389.5556, "col1": 389.7111, "col2": "HandToWrist", "col3": "FailOnTouchObstacleShort", "col4": "KightCate"}, {"Unnamed: 0": "37", "col0": 389.5889, "col1": 389.7556, "col2": "HandToWrist", "col3": "FailOnTouchObstacleLong", "col4": "RightGate"}, {"Unnamed: 0": 38, "col0": 389.6111, "col1": 389.7222, "col2": "Hand", "col3": "FailOnTouchObstacleShort", "col4": "RightGate"}, {"Unnamed: 0": 39, "col0": 389.6444, "col1": 389.7556, "col2": "Hand", "col3": "FailOnTouchObstacleLong", "col4": "RightGate"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Unnamed: 0":0,"col0":380.5549,"col1":381.3475,"col2":"Hand","col3":"FailOnTouchObstacleMed","col4":"PickedUp"},{"Unnamed: 0":1,"col0":380.7,"col1":381.2222,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":2,"col0":381.2333,"col1":381.2444,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":3,"col0":382.4,"col1":382.9667,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"Pushed"},{"Unnamed: 0":4,"col0":382.4778,"col1":382.9222,"col2":"Hand","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":5,"col0":382.4778,"col1":382.9111,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":6,"col0":383.0455,"col1":384.0956,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"PickedUp"},{"Unnamed: 0":7,"col0":384.5334,"col1":384.7111,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":8,"col0":384.5778,"col1":384.8111,"col2":"HandToWrist","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":9,"col0":384.6111,"col1":384.7556,"col2":"Hand","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":10,"col0":384.6333,"col1":384.8333,"col2":"HandToWrist","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":11,"col0":384.6889,"col1":389.6667,"col2":"WristToElbow","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":12,"col0":384.7,"col1":384.9111,"col2":"Hand","col3":"FailOnTouchObstacleMed","col4":"LeftGate"},{"Unnamed: 0":13,"col0":384.7111,"col1":386.3889,"col2":"WristToElbow","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":14,"col0":384.7333,"col1":385.5222,"col2":"HandToWrist","col3":"FailOnTouchObstacleMed","col4":"LeftGate"},{"Unnamed: 0":15,"col0":384.8111,"col1":389.5556,"col2":"WristToElbow","col3":"FailOnTouchObstacleMed","col4":"LeftGate"},{"Unnamed: 0":16,"col0":385.4667,"col1":387.6889,"col2":"Hand","col3":"FailOnTouchObstacleLong-1","col4":"Pushed"},{"Unnamed: 0":17,"col0":385.5445,"col1":388.3556,"col2":"Hand","col3":"FailOnTouchObstacleMed-1","col4":"RightGate"},{"Unnamed: 0":18,"col0":385.5556,"col1":388.3556,"col2":"Hand","col3":"FailOnTouchObstacleShort-1","col4":"LeftGate"},{"Unnamed: 0":19,"col0":385.5556,"col1":386.0889,"col2":"Hand","col3":"FailOnTouchObstacleLong-1","col4":"LeftGate"},{"Unnamed: 0":20,"col0":385.8604,"col1":387.48,"col2":"Hand","col3":"FailOnTouchObstacleLong-1","col4":"PickedUp"},{"Unnamed: 0":21,"col0":385.8667,"col1":385.8889,"col2":"Hand","col3":"FailOnTouchObstacleLong-1","col4":"RightGate"},{"Unnamed: 0":22,"col0":385.9111,"col1":387.6333,"col2":"Hand","col3":"FailOnTouchObstacleLong-1","col4":"RightGate"},{"Unnamed: 0":23,"col0":386.4,"col1":387.9111,"col2":"HandToWrist","col3":"FailOnTouchObstacleShort-1","col4":"LeftGate"},{"Unnamed: 0":24,"col0":386.4,"col1":387.9111,"col2":"HandToWrist","col3":"FailOnTouchObstacleMed-1","col4":"RightGate"},{"Unnamed: 0":25,"col0":388.0,"col1":389.6444,"col2":"WristToElbow","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":26,"col0":388.0667,"col1":388.6556,"col2":"HandToWrist","col3":"FailOnTouchObstacleMed-1","col4":"RightGate"},{"Unnamed: 0":27,"col0":388.0667,"col1":388.5334,"col2":"HandToWrist","col3":"FailOnTouchObstacleShort-1","col4":"LeftGate"},{"Unnamed: 0":28,"col0":388.3222,"col1":389.3333,"col2":"WristToElbow","col3":"FailOnTouchObstacleShort-1","col4":"LeftGate"},{"Unnamed: 0":29,"col0":388.3222,"col1":389.3445,"col2":"WristToElbow","col3":"FailOnTouchObstacleMed-1","col4":"RightGate"},{"Unnamed: 0":30,"col0":388.8333,"col1":389.1556,"col2":"Hand","col3":"Target","col4":"Pushed"},{"Unnamed: 0":31,"col0":389.0439,"col1":null,"col2":"Hand","col3":"Target","col4":"PickedUp"},{"Unnamed: 0":32,"col0":389.1778,"col1":389.4667,"col2":"HandToWrist","col3":"FailOnTouchObstacleMed-1","col4":"RightGate"},{"Unnamed: 0":33,"col0":389.3111,"col1":389.4778,"col2":"Hand","col3":"FailOnTouchObstacleMed-1","col4":"RightGate"},{"Unnamed: 0":34,"col0":389.4667,"col1":389.6222,"col2":"HandToWrist","col3":"FailOnTouchObstacleMed","col4":"LeftGate"},{"Unnamed: 0":35,"col0":389.5111,"col1":389.6333,"col2":"Hand","col3":"FailOnTouchObstacleMed","col4":"LeftGate"},{"Unnamed: 0":36,"col0":389.5556,"col1":389.7111,"col2":"HandToWrist","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":37,"col0":389.5889,"col1":389.7556,"col2":"HandToWrist","col3":"FailOnTouchObstacleLong","col4":"RightGate"},{"Unnamed: 0":38,"col0":389.6111,"col1":389.7222,"col2":"Hand","col3":"FailOnTouchObstacleShort","col4":"RightGate"},{"Unnamed: 0":39,"col0":389.6444,"col1":389.7556,"col2":"Hand","col3":"FailOnTouchObstacleLong","col4":"RightGate"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "yellow or white, creamy or solid food made from the pressed curds of milk": "Category:Yak's-milk cheeses"}, {"Prop0": "Prop2", "yellow or white, creamy or solid food made from the pressed curds of milk": "yak milk"}, {"Prop0": "Prop3", "yellow or white, creamy or solid food made from the pressed curds of milk": "Nepal"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","yellow or white, creamy or solid food made from the pressed curds of milk":"Category:Yak's-milk cheeses"},{"Prop0":"Prop2","yellow or white, creamy or solid food made from the pressed curds of milk":"yak milk"},{"Prop0":"Prop3","yellow or white, creamy or solid food made from the pressed curds of milk":"Nepal"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "type of dish comprised of fish": "Bengali cuisine"}, {"Prop0": "Prop2", "type of dish comprised of fish": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","type of dish comprised of fish":"Bengali cuisine"},{"Prop0":"Prop2","type of dish comprised of fish":"India"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "type of dish comprised of fish": "Bengali cuisine"}, {"Prop0": "Prop2", "type of dish comprised of fish": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","type of dish comprised of fish":"Bengali cuisine"},{"Prop0":"Prop2","type of dish comprised of fish":"India"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "food produced from or containing the milk of mammals": "Pribin\u00e1\u010dek"}, {"Prop0": "Prop2", "food produced from or containing the milk of mammals": "Savencia Fromage & Dairy Czech Republic"}, {"Prop0": "Prop3", "food produced from or containing the milk of mammals": "Czech Republic"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","food produced from or containing the milk of mammals":"Pribináček"},{"Prop0":"Prop2","food produced from or containing the milk of mammals":"Savencia Fromage & Dairy Czech Republic"},{"Prop0":"Prop3","food produced from or containing the milk of mammals":"Czech Republic"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "type of dish comprised of fish": "Bengali cuisine"}, {"Prop0": "Prop2", "type of dish comprised of fish": "India"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","type of dish comprised of fish":"Bengali cuisine"},{"Prop0":"Prop2","type of dish comprised of fish":"India"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Dodge Challenger", "col1": "Dodge", "col2": "sports car"}, {"col0": "Volkswagen Transporter", "col1": "Volkswagen", "col2": "van"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Dodge Challenger","col1":"Dodge","col2":"sports car"},{"col0":"Volkswagen Transporter","col1":"Volkswagen","col2":"van"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "primarily liquid food": "Kazakh cuisine, Uzbek cuisine, Turkmen cuisine, Tajik cuisine, Caucasian cuisine, Kyrgyz cuisine"}, {"Prop0": "Prop2", "primarily liquid food": "ciorb\u0103"}, {"Prop0": "Prop3", "primarily liquid food": "Chorba"}, {"Prop0": "Prop4", "primarily liquid food": "meat"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","primarily liquid food":"Kazakh cuisine, Uzbek cuisine, Turkmen cuisine, Tajik cuisine, Caucasian cuisine, Kyrgyz cuisine"},{"Prop0":"Prop2","primarily liquid food":"ciorbă"},{"Prop0":"Prop3","primarily liquid food":"Chorba"},{"Prop0":"Prop4","primarily liquid food":"meat"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
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http://commons.wikimedia.org/wiki/Special:FilePath/Pa-%E0%A8%B8%E0%A9%87%E0%A8%AC.ogg, http://commons.wikimedia.org/wiki/Special:FilePath/Roh-put%C3%A8r-pom.ogg, http://commons.wikimedia.org/wiki/Special:FilePath/LL-Q5137%20%28guj%29-Sushant%20savla-%E0%AA%B8%E0%AA%AB%E0%AA%B0%E0%AA%9C%E0%AA%A8.wav, http://commons.wikimedia.org/wiki/Special:FilePath/Pa-%E0%A8%B8%E0%A9%87%E0%A8%AC.ogg, http://commons.wikimedia.org/wiki/Special:FilePath/Roh-put%C3%A8r-pom.ogg, http://commons.wikimedia.org/wiki/Special:FilePath/LL-Q5137%20%28guj%29-Sushant%20savla-%E0%AA%B8%E0%AA%AB%E0%AA%B0%E0%AA%9C%E0%AA%A8.wav, http://commons.wikimedia.org/wiki/Special:FilePath/Pa-%E0%A8%B8%E0%A9%87%E0%A8%AC.ogg, http://commons.wikimedia.org/wiki/Special:FilePath/Roh-put%C3%A8r-pom.ogg, http://commons.wikimedia.org/wiki/Special:FilePath/LL-Q5137%20%28guj%29-Sushant%20savla-%E0%AA%B8%E0%AA%AB%E0%AA%B0%E0%AA%9C%E0%AA%A8.wav, http://commons.wikimedia.org/wiki/Special:FilePath/Pa-%E0%A8%B8%E0%A9%87%E0%A8%AC.ogg, 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🍎, 🍎, 🍎, 🍎, 🍎, 🍎, 🍎","col4":"6464948011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011, 724694011","col5":5781,"col6":"Wikipedia:List of articles all languages should have, Wikipedia:Vital articles\/Level\/4, Wikipedia:Vital articles\/Level\/4, Wikipedia:Vital articles\/Level\/4, Wikipedia:Vital articles\/Level\/4, Wikipedia:Vital articles\/Level\/4, Wikipedia:Vital articles\/Level\/4, Wikipedia:Vital articles\/Level\/4","col7":"Entity[\"FoodType\", \"Apple\"]","col8":"?"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Nordstern", "col1": "Germany"}, {"col0": "Continentale", "col1": "Germany"}, {"col0": "Colonia", "col1": "Germany"}, {"col0": "Mutex", "col1": "France"}, {"col0": "AXA", "col1": "France"}, {"col0": "De Nederlanden van 1845", "col1": "Netherlands"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Nordstern","col1":"Germany"},{"col0":"Continentale","col1":"Germany"},{"col0":"Colonia","col1":"Germany"},{"col0":"Mutex","col1":"France"},{"col0":"AXA","col1":"France"},{"col0":"De Nederlanden van 1845","col1":"Netherlands"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "eating that takes place at a specific time": "Cream tea"}, {"Prop0": "Prop2", "eating that takes place at a specific time": "tea"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","eating that takes place at a specific time":"Cream tea"},{"Prop0":"Prop2","eating that takes place at a specific time":"tea"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "anna", "col1": 120.24}, {"col0": "Para", "col1": 30.3}, {"col0": "Voleat", "col1": 70.63}, {"col0": "Shwckle", "col1": 59.76}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"anna","col1":120.24},{"col0":"Para","col1":30.3},{"col0":"Voleat","col1":70.63},{"col0":"Shwckle","col1":59.76}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Met Museum artworks on display"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "56.171.154"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Metropolitan Museum of Art, The Cloisters"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Met Museum artworks on display"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"56.171.154"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Metropolitan Museum of Art, The Cloisters"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Xeldon", "col1": 1.1011}, {"col0": "ontpartre", "col1": 1.683}, {"col0": "lida", "col1": 0.36815}, {"col0": "Eobw", "col1": "3.q43SZ"}, {"col0": "Meachap", "col1": 1.27889}, {"col0": "Vascoy", "col1": null}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Xeldon","col1":1.1011},{"col0":"ontpartre","col1":1.683},{"col0":"lida","col1":0.36815},{"col0":"Eobw","col1":3.94352},{"col0":"Meachap","col1":1.27889},{"col0":"Vascoy","col1":null}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop|", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesia"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesia"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesia, Central Java"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesian cuisine"}, {"Prop0": "Prop5", "specific food preparation with cooking finished, and ready to eat, or be served": "Culture of Indonesia"}, {"Prop0": "Prop6", "specific food preparation with cooking finished, and ready to eat, or be served": "frog legs"}, {"Prop0": "Prop7", "specific food preparation with cooking finished, and ready to eat, or be served": "Swikee"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesia"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesia"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesia, Central Java"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesian cuisine"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"Culture of Indonesia"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"frog legs"},{"Prop0":"Prop7","specific food preparation with cooking finished, and ready to eat, or be served":"Swikee"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "TKFK()", "col1": "Ullucus tuberosus", "col2": "Ullucus tuberosus", "col3": "Ullucus", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=40814", "col6": "Category:Ullucus tuberosus", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER1", "col1": "Macrotyloma uniflorum", "col2": "Macroty1oma unif1orum", "col3": "Macrotyloma", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=23076", "col6": "Category:Macrotyloma uniflorum", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER2", "col1": "Crithmum maritimum", "col2": "Crithmum maritimum", "col3": "Crithmum", "col4": "species", "col5": "https://npgsWeb.ars~grin.goV/gring1oba1/ta*onomydetai1.asp*?id=4()ZZ37", "col6": "Category:Crithmum maritimum", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER3", "col1": "Solanum incanum", "col2": "Solanum incanum", "col3": "Solanum subg. Leptostemonum", "col4": "species", "col5": "https://npgsWeb.ars~grin.goV/gring1oba1/ta*onomydetai1.asp*?id=|()|4&&", "col6": "Category:Solanum incanum", "col7": "?"}, {"col0": "TRER4", "col1": "Aegopodium podagraria", "col2": "Aegopodium podagraria", "col3": "Aegopodium", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=102037", "col6": "Category:Aegopodium podagraria", "col7": "?"}, {"col0": "TRER5", "col1": "Brassica juncea", "col2": "Brassica juncea", "col3": "Brassica", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=7654", "col6": "Category:Brassica juncea", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER6", "col1": "Tragopogon pratensis", "col2": "Tragopogon pratensis", "col3": "Tragopogon", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=36818", "col6": "Category:Tragopogon pratensis", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER7", "col1": "Brassica oleracea", "col2": "Brassica oleracea", "col3": "Brassica", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=7668", "col6": "Category:Brassica oleracea", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER8", "col1": "Bambusoideae", "col2": "Bambusoideae", "col3": "Poaceae", "col4": "subfamily", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomyfamily.aspx?type=subfamily&id=1465", "col6": "Category:Bambusoideae", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER9", "col1": "Corchorus olitorius", "col2": "Corchorus olitorius", "col3": "Gorchorus", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=11458", "col6": "Category:Corchorus olitorius", "col7": "?"}, {"col0": "TRER10", "col1": "Chaerophyllum bulbosum", "col2": "Chaerophyllum bulbosum", "col3": "Chaerophyllum", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=10020", "col6": "Category:Chaerophyllum bulbosum", "col7": "?"}, {"col0": "TRER11", "col1": "Cyamopsis tetragonoloba", "col2": "Cyamopsis tetragonoloba", "col3": "Cyamopsis", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=12701", "col6": "Category:Cyamopsis tetragonoloba", "col7": "?"}, {"col0": "TRER12", "col1": "Stachys affinis", "col2": "Stachys affinis", "col3": "Stachys", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=35402", "col6": "Category:Stachys affinis", "col7": "?"}, {"col0": "TKFK|3", "col1": "Vigna radiata", "col2": "Vigna radiata", "col3": "Vigna", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=41629", "col6": "Category:Vigna radiata", "col7": "?"}, {"col0": "TRER14", "col1": "Nasturtium officinale", "col2": "Nasturtium officinale", "col3": "Nasturtium", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=25072", "col6": "Category:Nasturtium officinale", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER15", "col1": "Scorzonera hispanica", "col2": "5corzonera hispanica", "col3": "Scorzonera", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=33412", "col6": "Category:Scorzonera hispanica", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER16", "col1": "8rassica", "col2": "Brassica", "col3": "Brassiceae", "col4": "genus", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomygenus.aspx?id=1687", "col6": "Category:Brassica", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER17", "col1": "Rumex acetosa", "col2": "Rumex acetosa", "col3": "Rumex", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=32518", "col6": "Category:Rumex acetosa", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER18", "col1": "Sonchus oleraceus", "col2": "Sonchus oleraceus", "col3": "5onchus", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=34939", "col6": "Category:Sonchus oleraceus", "col7": "?"}, {"col0": "TRER19", "col1": "Atrip1e* hortensis", "col2": "Atriplex hortensis", "col3": "Atriplex", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=6017", "col6": "Category:Atriplex hortensis", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER20", "col1": "Lycium barbarum", "col2": "Lycium barbarum", "col3": "Lycium", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=22939", "col6": "Category:Lycium barbarum", "col7": "?"}, {"col0": "TRER21", "col1": "Arracacia xanthorrhiza", "col2": "Arracacia xanthorrhiza", "col3": "Arracacia", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=4256", "col6": "Category:Arracacia xanthorrhiza", "col7": "?"}, {"col0": "TRER22", "col1": "Acmella oleracea", "col2": "Acmella oleracea", "col3": "Acme11a", "col4": "species", "col5": "https://npgsWeb.ars~grin.goV/gring1oba1/ta*onomydetai1.asp*?id=3||&G()", "col6": "Category:Acmella oleracea", "col7": "?"}, {"col0": "TRER23", "col1": "Salicornia europaea", "col2": "Salicornia europaea", "col3": "Salicornia", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=412197", "col6": "Category:Salicornia europaea", "col7": "?"}, {"col0": "TRER24", "col1": "Capsicum", "col2": "Capsicum", "col3": "Capsiceae", "col4": "genus", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomygenus.aspx?id=2056", "col6": "Gategory:Gapsicum, Q&3433&&, Q&3433&&", "col7": "?"}, {"col0": "TRER25", "col1": "Atriplex prostrata", "col2": "Atriplex prostrata", "col3": "Atriplex sect. Teutliopsis", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=6036", "col6": "Category:Atriplex prostrata", "col7": "?"}, {"col0": "TRER26", "col1": "Apium graveolens", "col2": "Apium graveolens", "col3": "Apium", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=300034", "col6": "Category:Apium graveolens", "col7": "?"}, {"col0": "TRER27", "col1": "Ipomoea aquatica", "col2": "kangkung", "col3": "Ipomoea", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=20138", "col6": "Category:Ipomoea aquatica", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER28", "col1": "Vigna angularis", "col2": "Vigna angularis", "col3": "Vigna", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=41598", "col6": "Category:Vigna angularis", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TKFKZq", "col1": "Corchorus", "col2": "Corchorus", "col3": "CreWioideae", "col4": "genus", "col5": "https://npgsWeb.ars~grin.goV/gring1oba1/ta*onomygenus.asp*?id=Zq|&", "col6": "Category:Corchorus", "col7": "?"}, {"col0": "TRER30", "col1": "Psophocarpus tetragonolobus", "col2": "Psophocarpus tetragonolobus", "col3": "Psophocarpus", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=30222", "col6": "Category:Psophocarpus tetragonolobus", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER31", "col1": "Sesbania grandiflora", "col2": "Sesbania grandiflora", "col3": "Sesbania", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=33770", "col6": "Category:Sesbania grandiflora", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER32", "col1": "Brassica oleracea var. gongylodes", "col2": "8rassica o1eracea Var. gongy1odes", "col3": "Brassica oleracea", "col4": "variety", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=7676", "col6": "Category:Brassica oleracea var. gongylodes", "col7": "?"}, {"col0": "TRER33", "col1": "Canavalia gladiata", "col2": "Canavalia gladiata", "col3": "Canavalia", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=8829", "col6": "Gategory:GanaVa1ia g1adiata", "col7": "?"}, {"col0": "TRER34", "col1": "Cucurbita maxima", "col2": "Cucurbita maxima", "col3": "Cucurbita", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=12597", "col6": "Category:Cucurbita maxima", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TKFK3S", "col1": "Foeniculum vulgare", "col2": "Foeniculum vulgare", "col3": "Foeniculum", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=300219", "col6": "Category:Foeniculum vulgare", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER36", "col1": "Pachyrhizus erosus", "col2": "Pachyrhizus erosus", "col3": "Pachyrhizus", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=26286", "col6": "Category:Pachyrhizus erosus", "col7": "?"}, {"col0": "TKFK37", "col1": "Oxalis tuberosa", "col2": "Oxalis tuberosa", "col3": "Oxalis", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=26219", "col6": "Category:Oxalis tuberosa", "col7": "?"}, {"col0": "TRER38", "col1": "Beta vulgaris", "col2": "Beta vulgaris", "col3": "Beta sect. Beta", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=300073", "col6": "Category:Beta vulgaris", "col7": "?"}, {"col0": "TKFK3q", "col1": "Sium sisarum", "col2": "Sium sisarum", "col3": "Sium", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=34477", "col6": "Category:Sium sisarum", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER40", "col1": "Claytonia perfoliata", "col2": "Claytonia perfoliata", "col3": "Claytonia", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=316941", "col6": "Category:Claytonia perfoliata", "col7": "edib1e p1ant or part of a p1ant, inVo1Ved in cooking"}, {"col0": "TRER41", "col1": "Stellaria media", "col2": "Stellaria media", "col3": "Stellaria", "col4": "species", "col5": "https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=310864", "col6": "Category:Stellaria media", "col7": "edible plant or part of a plant, involved in cooking"}, {"col0": "TRER42", "col1": "Broccoli", "col2": "Brassica oleracea var. italica", "col3": "Brassica oleracea", "col4": "variety", "col5": "https://npgsWeb.ars~grin.goV/gring1oba1/ta*onomydetai1.asp*?id=|()S447", "col6": "Category:Broccoli", "col7": "?"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"TRER0","col1":"Ullucus tuberosus","col2":"Ullucus tuberosus","col3":"Ullucus","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=40814","col6":"Category:Ullucus tuberosus","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER1","col1":"Macrotyloma uniflorum","col2":"Macrotyloma uniflorum","col3":"Macrotyloma","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=23076","col6":"Category:Macrotyloma uniflorum","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER2","col1":"Crithmum maritimum","col2":"Crithmum maritimum","col3":"Crithmum","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=402237","col6":"Category:Crithmum maritimum","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER3","col1":"Solanum incanum","col2":"Solanum incanum","col3":"Solanum subg. Leptostemonum","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=101488","col6":"Category:Solanum incanum","col7":"?"},{"col0":"TRER4","col1":"Aegopodium podagraria","col2":"Aegopodium podagraria","col3":"Aegopodium","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=102037","col6":"Category:Aegopodium podagraria","col7":"?"},{"col0":"TRER5","col1":"Brassica juncea","col2":"Brassica juncea","col3":"Brassica","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=7654","col6":"Category:Brassica juncea","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER6","col1":"Tragopogon pratensis","col2":"Tragopogon pratensis","col3":"Tragopogon","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=36818","col6":"Category:Tragopogon pratensis","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER7","col1":"Brassica oleracea","col2":"Brassica oleracea","col3":"Brassica","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=7668","col6":"Category:Brassica oleracea","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER8","col1":"Bambusoideae","col2":"Bambusoideae","col3":"Poaceae","col4":"subfamily","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomyfamily.aspx?type=subfamily&id=1465","col6":"Category:Bambusoideae","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER9","col1":"Corchorus olitorius","col2":"Corchorus olitorius","col3":"Corchorus","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=11458","col6":"Category:Corchorus olitorius","col7":"?"},{"col0":"TRER10","col1":"Chaerophyllum bulbosum","col2":"Chaerophyllum bulbosum","col3":"Chaerophyllum","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=10020","col6":"Category:Chaerophyllum bulbosum","col7":"?"},{"col0":"TRER11","col1":"Cyamopsis tetragonoloba","col2":"Cyamopsis tetragonoloba","col3":"Cyamopsis","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=12701","col6":"Category:Cyamopsis tetragonoloba","col7":"?"},{"col0":"TRER12","col1":"Stachys affinis","col2":"Stachys affinis","col3":"Stachys","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=35402","col6":"Category:Stachys affinis","col7":"?"},{"col0":"TRER13","col1":"Vigna radiata","col2":"Vigna radiata","col3":"Vigna","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=41629","col6":"Category:Vigna radiata","col7":"?"},{"col0":"TRER14","col1":"Nasturtium officinale","col2":"Nasturtium officinale","col3":"Nasturtium","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=25072","col6":"Category:Nasturtium officinale","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER15","col1":"Scorzonera hispanica","col2":"Scorzonera hispanica","col3":"Scorzonera","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=33412","col6":"Category:Scorzonera hispanica","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER16","col1":"Brassica","col2":"Brassica","col3":"Brassiceae","col4":"genus","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomygenus.aspx?id=1687","col6":"Category:Brassica","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER17","col1":"Rumex acetosa","col2":"Rumex acetosa","col3":"Rumex","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=32518","col6":"Category:Rumex acetosa","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER18","col1":"Sonchus oleraceus","col2":"Sonchus oleraceus","col3":"Sonchus","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=34939","col6":"Category:Sonchus oleraceus","col7":"?"},{"col0":"TRER19","col1":"Atriplex hortensis","col2":"Atriplex hortensis","col3":"Atriplex","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=6017","col6":"Category:Atriplex hortensis","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER20","col1":"Lycium barbarum","col2":"Lycium barbarum","col3":"Lycium","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=22939","col6":"Category:Lycium barbarum","col7":"?"},{"col0":"TRER21","col1":"Arracacia xanthorrhiza","col2":"Arracacia xanthorrhiza","col3":"Arracacia","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=4256","col6":"Category:Arracacia xanthorrhiza","col7":"?"},{"col0":"TRER22","col1":"Acmella oleracea","col2":"Acmella oleracea","col3":"Acmella","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=311860","col6":"Category:Acmella oleracea","col7":"?"},{"col0":"TRER23","col1":"Salicornia europaea","col2":"Salicornia europaea","col3":"Salicornia","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=412197","col6":"Category:Salicornia europaea","col7":"?"},{"col0":"TRER24","col1":"Capsicum","col2":"Capsicum","col3":"Capsiceae","col4":"genus","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomygenus.aspx?id=2056","col6":"Category:Capsicum, Q8343388, Q8343388","col7":"?"},{"col0":"TRER25","col1":"Atriplex prostrata","col2":"Atriplex prostrata","col3":"Atriplex sect. Teutliopsis","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=6036","col6":"Category:Atriplex prostrata","col7":"?"},{"col0":"TRER26","col1":"Apium graveolens","col2":"Apium graveolens","col3":"Apium","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=300034","col6":"Category:Apium graveolens","col7":"?"},{"col0":"TRER27","col1":"Ipomoea aquatica","col2":"kangkung","col3":"Ipomoea","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=20138","col6":"Category:Ipomoea aquatica","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER28","col1":"Vigna angularis","col2":"Vigna angularis","col3":"Vigna","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=41598","col6":"Category:Vigna angularis","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER29","col1":"Corchorus","col2":"Corchorus","col3":"Grewioideae","col4":"genus","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomygenus.aspx?id=2918","col6":"Category:Corchorus","col7":"?"},{"col0":"TRER30","col1":"Psophocarpus tetragonolobus","col2":"Psophocarpus tetragonolobus","col3":"Psophocarpus","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=30222","col6":"Category:Psophocarpus tetragonolobus","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER31","col1":"Sesbania grandiflora","col2":"Sesbania grandiflora","col3":"Sesbania","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=33770","col6":"Category:Sesbania grandiflora","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER32","col1":"Brassica oleracea var. gongylodes","col2":"Brassica oleracea var. gongylodes","col3":"Brassica oleracea","col4":"variety","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=7676","col6":"Category:Brassica oleracea var. gongylodes","col7":"?"},{"col0":"TRER33","col1":"Canavalia gladiata","col2":"Canavalia gladiata","col3":"Canavalia","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=8829","col6":"Category:Canavalia gladiata","col7":"?"},{"col0":"TRER34","col1":"Cucurbita maxima","col2":"Cucurbita maxima","col3":"Cucurbita","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=12597","col6":"Category:Cucurbita maxima","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER35","col1":"Foeniculum vulgare","col2":"Foeniculum vulgare","col3":"Foeniculum","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=300219","col6":"Category:Foeniculum vulgare","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER36","col1":"Pachyrhizus erosus","col2":"Pachyrhizus erosus","col3":"Pachyrhizus","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=26286","col6":"Category:Pachyrhizus erosus","col7":"?"},{"col0":"TRER37","col1":"Oxalis tuberosa","col2":"Oxalis tuberosa","col3":"Oxalis","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=26219","col6":"Category:Oxalis tuberosa","col7":"?"},{"col0":"TRER38","col1":"Beta vulgaris","col2":"Beta vulgaris","col3":"Beta sect. Beta","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=300073","col6":"Category:Beta vulgaris","col7":"?"},{"col0":"TRER39","col1":"Sium sisarum","col2":"Sium sisarum","col3":"Sium","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=34477","col6":"Category:Sium sisarum","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER40","col1":"Claytonia perfoliata","col2":"Claytonia perfoliata","col3":"Claytonia","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=316941","col6":"Category:Claytonia perfoliata","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER41","col1":"Stellaria media","col2":"Stellaria media","col3":"Stellaria","col4":"species","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=310864","col6":"Category:Stellaria media","col7":"edible plant or part of a plant, involved in cooking"},{"col0":"TRER42","col1":"Broccoli","col2":"Brassica oleracea var. italica","col3":"Brassica oleracea","col4":"variety","col5":"https:\/\/npgsweb.ars-grin.gov\/gringlobal\/taxonomydetail.aspx?id=105447","col6":"Category:Broccoli","col7":"?"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "ZVNI0", "col1": 2018, "col2": "Entity[\"Food\", \"FennelSeed::7qydb\"], Entity[\"FoodType\", \"FennelSeed\"], Entity[\"FoodType\", \"FennelSeed\"], Entity[\"FoodType\", \"FennelSeed\"], Entity[\"FoodType\", \"FennelSeed\"]", "col3": "C1145581", "col4": "16321621", "col5": "dried seed, fruit, root, bark, or vegetable substance primarily used for flavoring, coloring or preserving food"}, {"col0": "ZVNI1", "col1": "Z()()q", "col2": "Entity[\"Food\", \"ChilliPowder::5n2t4\"]", "col3": "C0475622", "col4": "16321541, 16321531, 6492421011, 16321531, 6492421011", "col5": "?"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"ZVNI0","col1":2018,"col2":"Entity[\"Food\", \"FennelSeed::7qydb\"], Entity[\"FoodType\", \"FennelSeed\"], Entity[\"FoodType\", \"FennelSeed\"], Entity[\"FoodType\", \"FennelSeed\"], Entity[\"FoodType\", \"FennelSeed\"]","col3":"C1145581","col4":"16321621","col5":"dried seed, fruit, root, bark, or vegetable substance primarily used for flavoring, coloring or preserving food"},{"col0":"ZVNI1","col1":2009,"col2":"Entity[\"Food\", \"ChilliPowder::5n2t4\"]","col3":"C0475622","col4":"16321541, 16321531, 6492421011, 16321531, 6492421011","col5":"?"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "kind of liquid which is specifically prepared for human consumption": "Khoday Group"}, {"Prop0": "Prop2", "kind of liquid which is specifically prepared for human consumption": "India"}, {"Prop0": "Prop3", "kind of liquid which is specifically prepared for human consumption": "1967-01-01T00:00:00Z"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","kind of liquid which is specifically prepared for human consumption":"Khoday Group"},{"Prop0":"Prop2","kind of liquid which is specifically prepared for human consumption":"India"},{"Prop0":"Prop3","kind of liquid which is specifically prepared for human consumption":"1967-01-01T00:00:00Z"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "WZVV0", "col1": "1940-01-01T00:00:00Z", "col2": "Nucrem", "col3": "course that concludes a meal; usually sweet"}, {"col0": "WZVV1", "col1": "2009-01-01T00:00:00Z", "col2": "Mantecol", "col3": "?"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"WZVV0","col1":"1940-01-01T00:00:00Z","col2":"Nucrem","col3":"course that concludes a meal; usually sweet"},{"col0":"WZVV1","col1":"2009-01-01T00:00:00Z","col2":"Mantecol","col3":"?"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Planta dos Terrenos Junto \u00e0 Vila de Santp Amaro", "col1": 94.8838}, {"col0": "Reprodu\u00e7\u00e3o de Ilustra\u00e7\u00e3o: S\u00edtio no Prae\u00ed (Vale do Para\u00edba)", "col1": 40.24}, {"col0": "Plata da Fazenda Barreiro Rio", "col1": 117.174}, {"col0": "Morro do Ch\u00e1 S\u00e3o#Paulo.", "col1": 85.833}, {"col0": "Reprodu\u00e7\u00e3o de Planta: Villa Sta. Catharipa", "col1": 23.2386}, {"col0": "Keprodu\u00e8\u00e3o de Mapa: Posi\u00e7\u00e3o da 5esmaria Gambuhy", "col1": 23.952}, {"col0": "Planta da Vlla Conde do Pinhal.", "col1": 91.364}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Planta dos Terrenos Junto à Vila de Santp Amaro","col1":94.8838},{"col0":"Reprodução de Ilustração: Sítio no Praeí (Vale do Paraíba)","col1":40.24},{"col0":"Plata da Fazenda Barreiro Rio","col1":117.174},{"col0":"Morro do Chá São#Paulo.","col1":85.833},{"col0":"Reprodução de Planta: Villa Sta. Catharipa","col1":23.2386},{"col0":"Reproduèão de Mapa: Posição da Sesmaria Cambuhy","col1":23.952},{"col0":"Planta da Vlla Conde do Pinhal.","col1":91.364}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "N1Djamena", "col1": 100.0}, {"col0": "Arganil", "col1": null}, {"col0": "Bombarral", "col1": null}, {"col0": "Fuzhou", "col1": 12250.72}, {"col0": "Tokyo", "col1": 2194.05}, {"col0": "Tehran", "col1": 686.0}, {"col0": "Cardiff", "col1": 140.3}, {"col0": "Edinburgh of the Seven Seas", "col1": null}, {"col0": "Bamako", "col1": 245.0}, {"col0": "Bandar Seri Begawan", "col1": 100360000.0}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"N'Djamena","col1":100.0},{"col0":"Arganil","col1":null},{"col0":"Bombarral","col1":null},{"col0":"Fuzhou","col1":12250.72},{"col0":"Tokyo","col1":2194.05},{"col0":"Tehran","col1":686.0},{"col0":"Cardiff","col1":140.3},{"col0":"Edinburgh of the Seven Seas","col1":null},{"col0":"Bamako","col1":245.0},{"col0":"Bandar Seri Begawan","col1":100360000.0}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "food, edible in the raw state": "Samer"}, {"Prop0": "Prop2", "food, edible in the raw state": "France"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","food, edible in the raw state":"Samer"},{"Prop0":"Prop2","food, edible in the raw state":"France"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "1ndonesia"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesia"}, {"Prop0": "Prop3", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesian cuisine"}, {"Prop0": "Prop4", "specific food preparation with cooking finished, and ready to eat, or be served": "Indonesia, Central Java, East Java, Special Region of Yogyakarta, Wonogiri, Wonosobo, Pacitan, Gunungkidul"}, {"Prop0": "Prop5", "specific food preparation with cooking finished, and ready to eat, or be served": "cassava root"}, {"Prop0": "Prop6", "specific food preparation with cooking finished, and ready to eat, or be served": "Culture of Indonesia"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesia"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesia"},{"Prop0":"Prop3","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesian cuisine"},{"Prop0":"Prop4","specific food preparation with cooking finished, and ready to eat, or be served":"Indonesia, Central Java, East Java, Special Region of Yogyakarta, Wonogiri, Wonosobo, Pacitan, Gunungkidul"},{"Prop0":"Prop5","specific food preparation with cooking finished, and ready to eat, or be served":"cassava root"},{"Prop0":"Prop6","specific food preparation with cooking finished, and ready to eat, or be served":"Culture of Indonesia"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "F. Goltz", "col1": "Hermann von Helmholtz", "col2": "G\u00f6ttingen Academy of Sciences"}, {"col0": "C. S. Weiss", "col1": "Abraham Gottlob Werner", "col2": "Koya1 Prussian Academy of 5ciences"}, {"col0": ". A. Rowland", "col1": "Hermann von Helmholtz", "col2": "Royal Society"}, {"col0": "R. Oppenheimer", "col1": "Max Born", "col2": "Royal Society"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"F. Goltz","col1":"Hermann von Helmholtz","col2":"Göttingen Academy of Sciences"},{"col0":"C. S. Weiss","col1":"Abraham Gottlob Werner","col2":"Royal Prussian Academy of Sciences"},{"col0":". A. Rowland","col1":"Hermann von Helmholtz","col2":"Royal Society"},{"col0":"R. Oppenheimer","col1":"Max Born","col2":"Royal Society"}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"col0": "Arthur Township", "col1": 1843, "col2": 31.3}, {"col0": "Danforth Township", "col1": 79, "col2": 36.3}, {"col0": "Lakewood Township", "col1": 2276, "col2": 27.8}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"col0":"Arthur Township","col1":1843,"col2":31.3},{"col0":"Danforth Township","col1":79,"col2":36.3},{"col0":"Lakewood Township","col1":2276,"col2":27.8}]<|eot_id|>
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are tasked with correcting a corrupted JSON table dataset that contains several errors introduced by an Optical Character Recognition (OCR) process. These errors include, but are not limited to:
1. Misspelled text: OCR misinterpretations have resulted in incorrect spellings or symbols replacing valid characters in text-based fields.
2. Incorrect numbers: Numeric values are misrepresented, with random characters or incomplete/incorrect digits.
3. Confused patterns: Specific symbols or substrings such as `|`, `&`, and non-numeric characters have been mistakenly introduced into numeric fields.
4. Partial data: Fragments of numeric or text-based fields are observed due to poor OCR interpretation.
Your goal is to recover the original structure and correct the data to reflect a valid and consistent table, ensuring accuracy and logical consistency based on contextual clues.
### Input:
A JSON string representing a corrupted table dataset. For example:
```json
[
{"col0": "Cuaru1hos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": "|G7S3G&", "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": "7G&", "col3": 168.0}
]
```
### Expected Output:
A corrected JSON string where all corrupted entries are fixed to reflect the intended original values. For example:
```json
[
{"col0": "Guarulhos", "col1": 1324781, "col2": 759, "col3": 318.014},
{"col0": "Mecca", "col1": 1675368, "col2": 277, "col3": 760.0},
{"col0": "Bandung", "col1": 2875673, "col2": 768, "col3": 168.0}
]
```
### Detailed Instructions:
1. Text Corrections:
- Correct misspelled or misinterpreted text in `col0` using contextual knowledge and expected patterns (e.g., "Cuaru1hos" should be "Guarulhos").
- Validate place names, city names, or any other text-based entries using common naming conventions.
2. Numeric Corrections:
- Remove non-numeric characters (`|`, `&`, letters, etc.) from fields expected to contain numbers (`col1`, `col2`, `col3`).
- Restore valid numerical patterns where digits are fragmented or missing (e.g., "|G7S3G&" to "1675368").
3. Pattern Alignment:
- Ensure consistency across fields. For example:
- `col2` values represent integer IDs or counts.
- `col3` values represent float or double-type numbers.
4. Logical Context Validation:
- If a column's values suggest specific constraints (e.g., a population column should have plausible ranges), ensure that the corrected values fall within reasonable bounds.
5. Output Format:
- The corrected JSON should strictly follow the format of the original input while replacing incorrect values.
6. Assumptions:
- Assume that the corrected data is logically consistent with the context of each field.
- If ambiguities arise, use reasonable guesses based on examples provided or logical patterns.
---
Evaluation:
The success of the task will be measured by how accurately the corrected JSON table aligns with the intended structure and content, ensuring complete recovery from OCR-related corruption.
<|eot_id|><|start_header_id|>user<|end_header_id|>[{"Prop0": "Prop1", "specific food preparation with cooking finished, and ready to eat, or be served": "Met Museum artworks on display"}, {"Prop0": "Prop2", "specific food preparation with cooking finished, and ready to eat, or be served": "Metropolitan Museum of Art, Islamic Art"}]<|eot_id|><|start_header_id|>assistant<|end_header_id|>[{"Prop0":"Prop1","specific food preparation with cooking finished, and ready to eat, or be served":"Met Museum artworks on display"},{"Prop0":"Prop2","specific food preparation with cooking finished, and ready to eat, or be served":"Metropolitan Museum of Art, Islamic Art"}]<|eot_id|>
|
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