MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| tracking_time           |
|:------------------------|
| 2019-05-06 03:00:00.000 |
| 2019-05-06 04:00:00.000 |
| 2019-05-06 13:30:00.000 |
| 2019-05-06 14:30:00.000 |
| 2019-05-06 15:30:00.000 |
| 2019-05-06 20:15:00.000 |
| 2019-05-06 20:45:00.000 |
| 2019-05-07 05:00:00.000 |
| 2019-05-07 04:00:00.000 |
| 2019-05-07 03:00:00.000 |
| 2019-05-07 06:00:00.000 |
| 2019-05-08 07:00:00.000 |
| 2019-05-08 05:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| sales stage   |
|:--------------|
| propose       |
| close         |
| qualify       |
| develop       |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| month   |
|:--------|
| jan     |
| feb     |
| mar     |
| apr     |
| may     |
| jun     |
| jul     |
| aug     |
| sep     |
| oct     |
| nov     |
| dec     |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| first_name   |
|:-------------|
| akshay       |
| pramod       |
| pritam       |
| deepak       |
| amit         |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| underwriting class name        |
|:-------------------------------|
| contingency                    |
| other professional lines       |
| political risks                |
| political violence             |
| aviation                       |
| pa                             |
| cyber                          |
| property d&f                   |
| marine liability               |
| energy liability               |
| excess casualty                |
| term life                      |
| specie                         |
| professional indemnity         |
| property fire open market      |
| property fire binders          |
| legacy                         |
| north american property treaty |
| hull                           |
| marine & energy excess of loss |
| cargo                          |
| energy offshore ex gom wind    |
| satellites om                  |
| product damage & breakdown     |
| political risk                 |
| satellites delegated           |
| international property treaty  |
| airlines/aerospace             |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| currency   |
|:-----------|
| rs.        |
| aed        |
| lkr        |
| tl         |
| qr         |
| p          |
| r          |
| r$         |
| £          |
| nz$        |
| idr        |
| $          |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| quarter   |
|:----------|
| q3 2013   |
| q1 2013   |
| q2 2013   |
| q4 2013   |
| q1 2012   |
| q3 2012   |
| q2 2012   |
| q4 2012   |
| q1 2014   |
| q2 2014   |
| q3 2014   |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| start date              |
|:------------------------|
| 2016-10-10 00:00:00.000 |
| 2015-10-13 00:00:00.000 |
| 2014-03-18 00:00:00.000 |
| 2013-09-03 00:00:00.000 |
| 2012-12-03 00:00:00.000 |
| 2011-11-10 00:00:00.000 |
| 2009-10-15 00:00:00.000 |
| 2008-10-21 00:00:00.000 |
| 2005-06-01 00:00:00.000 |
| 2003-10-10 00:00:00.000 |
| 2002-10-16 00:00:00.000 |
| 2001-10-17 00:00:00.000 |
| 2000-10-15 00:00:00.000 |
| 1999-10-20 00:00:00.000 |
| 1997-11-18 00:00:00.000 |
| 1996-09-17 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| estado_civil   |
|:---------------|
| solteiro       |
| casado         |
| outros         |
| viuvo          |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| date                    |
|:------------------------|
| 2022-02-27 00:00:00.000 |
| 2022-02-28 00:00:00.000 |
| 2022-03-01 00:00:00.000 |
| 2022-03-02 00:00:00.000 |
| 2022-03-03 00:00:00.000 |
| 2022-03-04 00:00:00.000 |
| 2022-03-05 00:00:00.000 |
| 2022-03-06 00:00:00.000 |
| 2022-03-07 00:00:00.000 |
| 2022-03-08 00:00:00.000 |
| 2022-03-09 00:00:00.000 |
| 2022-03-10 00:00:00.000 |
| 2022-03-11 00:00:00.000 |
| 2022-03-12 00:00:00.000 |
| 2022-03-13 00:00:00.000 |
| 2022-03-14 00:00:00.000 |
| 2022-03-15 00:00:00.000 |
| 2022-03-16 00:00:00.000 |
| 2022-03-17 00:00:00.000 |
| 2022-03-18 00:00:00.000 |
| 2022-03-19 00:00:00.000 |
| 2022-03-20 00:00:00.000 |
| 2022-03-21 00:00:00.000 |
| 2022-03-22 00:00:00.000 |
| 2022-03-23 00:00:00.000 |
| 2022-03-24 00:00:00.000 |
| 2022-03-25 00:00:00.000 |
| 2022-03-26 00:00:00.000 |
| 2022-03-27 00:00:00.000 |
| 2022-03-28 00:00:00.000 |
| 2022-03-29 00:00:00.000 |
| 2022-03-30 00:00:00.000 |
| 2022-03-31 00:00:00.000 |
| 2022-04-01 00:00:00.000 |
| 2022-04-02 00:00:00.000 |
| 2022-04-03 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| day   |
|:------|
| tue   |
| mon   |
| sun   |
| fri   |
| thu   |
| wed   |
| sat   |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| websiteurl                    |
|:------------------------------|
| http://www.adatum.com         |
| http://blueyonderairlines.com |
| http://www.tailspintoys.com   |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| date                    |
|:------------------------|
| 1914-01-29 00:00:00.000 |
| 1914-02-01 00:00:00.000 |
| 1914-02-18 00:00:00.000 |
| 1914-05-25 00:00:00.000 |
| 1914-08-02 00:00:00.000 |
| 1914-08-04 00:00:00.000 |
| 1914-08-07 00:00:00.000 |
| 1914-08-13 00:00:00.000 |
| 1914-08-21 00:00:00.000 |
| 1914-08-29 00:00:00.000 |
| 1914-09-22 00:00:00.000 |
| 1914-10-15 00:00:00.000 |
| 1914-10-16 00:00:00.000 |
| 1914-10-19 00:00:00.000 |
| 1914-10-29 00:00:00.000 |
| 1914-12-08 00:00:00.000 |
| 1914-12-17 00:00:00.000 |
| 1915-01-25 00:00:00.000 |
| 1915-01-27 00:00:00.000 |
| 1915-05-02 00:00:00.000 |
| 1915-05-03 00:00:00.000 |
| 1915-05-07 00:00:00.000 |
| 1915-05-08 00:00:00.000 |
| 1915-05-15 00:00:00.000 |
| 1915-05-29 00:00:00.000 |
| 1915-06-23 00:00:00.000 |
| 1915-08-18 00:00:00.000 |
| 1915-10-16 00:00:00.000 |
| 1915-11-10 00:00:00.000 |
| 1915-11-12 00:00:00.000 |
| 1915-12-06 00:00:00.000 |
| 1915-12-22 00:00:00.000 |
| 1915-12-24 00:00:00.000 |
| 1915-12-26 00:00:00.000 |
| 1916-01-17 00:00:00.000 |
| 1916-06-02 00:00:00.000 |
| 1916-06-03 00:00:00.000 |
| 1916-06-06 00:00:00.000 |
| 1916-06-16 00:00:00.000 |
| 1916-07-14 00:00:00.000 |
| 1916-08-16 00:00:00.000 |
| 1916-08-19 00:00:00.000 |
| 1916-12-21 00:00:00.000 |
| 1917-01-23 00:00:00.000 |
| 1917-02-09 00:00:00.000 |
| 1917-02-20 00:00:00.000 |
| 1917-03-13 00:00:00.000 |
| 1917-03-18 00:00:00.000 |
| 1917-03-23 00:00:00.000 |
| 1917-03-25 00:00:00.000 |
| 1917-03-27 00:00:00.000 |
| 1917-04-11 00:00:00.000 |
| 1917-04-14 00:00:00.000 |
| 1917-04-16 00:00:00.000 |
| 1917-07-31 00:00:00.000 |
| 1917-08-08 00:00:00.000 |
| 1917-09-04 00:00:00.000 |
| 1917-09-17 00:00:00.000 |
| 1917-11-26 00:00:00.000 |
| 1918-01-09 00:00:00.000 |
| 1918-03-22 00:00:00.000 |
| 1918-03-23 00:00:00.000 |
| 1918-06-09 00:00:00.000 |
| 1918-06-12 00:00:00.000 |
| 1918-08-20 00:00:00.000 |
| 1918-09-25 00:00:00.000 |
| 1918-09-27 00:00:00.000 |
| 1918-09-28 00:00:00.000 |
| 1918-10-01 00:00:00.000 |
| 1918-10-02 00:00:00.000 |
| 1918-11-11 00:00:00.000 |
| 1918-11-21 00:00:00.000 |
| 1919-05-27 00:00:00.000 |
| 1919-07-11 00:00:00.000 |
| 1920-01-08 00:00:00.000 |
| 1920-03-15 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| tipo imputacion   |
|:------------------|
| recx2             |
| ncb               |
| nca               |
| nceca             |
| notadb            |
| recm              |
| sin imputacion    |
| recmasi           |
| recasi            |
| ncbasi            |
| ncaasi            |
| ndcobr            |
| crg               |
| fcc               |
| fcb               |
| fccn              |
| fsl               |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| fourchette haute   |
|:-------------------|
| 12 035 €           |
| 7 795 €            |
| 11 797 €           |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| address1            |
|:--------------------|
| 108 lakeside court  |
| 2947 vine lane      |
| 620 woodside ct.    |
| 9539 glenside dr    |
| 101 candy rd.       |
| 6369 ellis street   |
| 9495 limewood place |
| 5980 icicle circle  |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| detention date          |
|:------------------------|
| 2019-11-11 11:11:00.000 |
| 2019-07-12 12:45:00.000 |
| 2019-09-18 12:01:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| product_url_in_platform_format      |
|:------------------------------------|
| https://sky.garden/product/pxdbh5q5 |
| https://sky.garden/product/pubvcrcq |
| https://sky.garden/product/pprj6gdu |
| https://sky.garden/product/p0k15ybl |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| error check       |
|:------------------|
| ok                |
| cur:null          |
| inf:null dth:null |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| product   |
|:----------|
| exchange  |
| skype     |
| teams     |
| yammer    |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| brand_c   |
|:----------|
| hak       |
| nsi       |
| hms       |
| mits      |
| proj      |
| adl       |
| msa       |
| ur        |
| ge f      |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| modifiedtime            |
|:------------------------|
| 2018-06-24 13:03:06.656 |
| 2018-06-17 02:22:46.933 |
| 2018-06-17 00:44:39.063 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| attribute    |
|:-------------|
| 1st quartile |
| 2nd quartile |
| 3rd quartile |
| 4th quartile |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| availabletime           |
|:------------------------|
| 2020-11-14 06:01:00.000 |
| 2020-11-13 13:32:00.000 |
| 2020-11-13 06:01:00.000 |
| 2020-11-12 17:05:59.000 |
| 2020-11-12 17:05:56.000 |
| 2020-11-11 03:05:50.000 |
| 2020-09-30 10:31:00.000 |
| 2020-09-12 15:46:00.000 |
| 2020-09-11 18:00:00.000 |
| 2020-09-10 18:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| date                    |
|:------------------------|
| 2026-05-02 00:00:00.000 |
| 2027-05-03 00:00:00.000 |
| 2027-05-06 00:00:00.000 |
| 2027-05-08 00:00:00.000 |
| 2027-05-09 00:00:00.000 |
| 2027-05-13 00:00:00.000 |
| 2027-05-16 00:00:00.000 |
| 2027-05-18 00:00:00.000 |
| 2027-05-19 00:00:00.000 |
| 2027-05-21 00:00:00.000 |
| 2027-05-22 00:00:00.000 |
| 2028-05-22 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| kraje            |
|:-----------------|
| košický kraj     |
| nitriansky kraj  |
| prešovský kraj   |
| trenčiansky kraj |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| type of product   |
|:------------------|
| fruits            |
| vegetables        |
| animal meat       |
| seafood meat      |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| estado            |
|:------------------|
| alberta           |
| ontario           |
| nova escocia      |
| california        |
| illinois          |
| texas             |
| tennessee         |
| vitoria           |
| nova gales do sul |
| queensland        |
| buenos aires      |
| faro              |
| braga             |
| porto             |
| baviera           |
| hesse             |
| berlim            |
| hamburgo          |
| lapônia           |
| escânia           |
| riksdagen         |
| distrito capital  |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| created                 |
|:------------------------|
| 2021-03-07 11:28:56.416 |
| 2021-03-07 11:29:40.370 |
| 2021-03-07 11:29:43.893 |
| 2021-03-07 11:30:10.230 |
| 2021-03-07 11:30:17.496 |
| 2021-03-07 11:30:28.596 |
| 2021-03-07 11:30:59.653 |
| 2021-03-07 11:31:03.043 |
| 2021-03-07 11:35:30.453 |
| 2021-03-07 11:35:35.563 |
| 2021-03-07 11:35:42.880 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| age group   |
|:------------|
| 65 and over |
| 30 to 39    |
| 50 to 64    |
| 40 to 49    |
| 18 to 29    |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| date                    |
|:------------------------|
| 2017-04-03 00:00:00.000 |
| 2017-04-04 00:00:00.000 |
| 2017-04-05 00:00:00.000 |
| 2017-04-06 00:00:00.000 |
| 2017-04-07 00:00:00.000 |
| 2017-04-08 00:00:00.000 |
| 2017-04-09 00:00:00.000 |
| 2017-04-10 00:00:00.000 |
| 2017-04-11 00:00:00.000 |
| 2017-04-12 00:00:00.000 |
| 2017-04-13 00:00:00.000 |
| 2017-04-14 00:00:00.000 |
| 2017-04-15 00:00:00.000 |
| 2017-04-16 00:00:00.000 |
| 2017-04-17 00:00:00.000 |
| 2017-04-18 00:00:00.000 |
| 2017-04-19 00:00:00.000 |
| 2017-04-20 00:00:00.000 |
| 2017-04-21 00:00:00.000 |
| 2017-04-22 00:00:00.000 |
| 2017-04-23 00:00:00.000 |
| 2017-04-24 00:00:00.000 |
| 2017-04-25 00:00:00.000 |
| 2017-04-26 00:00:00.000 |
| 2017-04-27 00:00:00.000 |
| 2017-04-28 00:00:00.000 |
| 2017-04-29 00:00:00.000 |
| 2017-04-30 00:00:00.000 |
| 2017-05-01 00:00:00.000 |
| 2017-05-02 00:00:00.000 |
| 2017-05-03 00:00:00.000 |
| 2017-05-04 00:00:00.000 |
| 2017-05-05 00:00:00.000 |
| 2017-05-06 00:00:00.000 |
| 2017-05-07 00:00:00.000 |
| 2017-05-08 00:00:00.000 |
| 2017-05-09 00:00:00.000 |
| 2017-05-10 00:00:00.000 |
| 2017-05-11 00:00:00.000 |
| 2017-05-12 00:00:00.000 |
| 2017-05-13 00:00:00.000 |
| 2017-05-14 00:00:00.000 |
| 2017-05-15 00:00:00.000 |
| 2017-05-16 00:00:00.000 |
| 2017-05-17 00:00:00.000 |
| 2017-05-18 00:00:00.000 |
| 2017-05-19 00:00:00.000 |
| 2017-05-20 00:00:00.000 |
| 2017-05-21 00:00:00.000 |
| 2017-05-22 00:00:00.000 |
| 2017-05-23 00:00:00.000 |
| 2017-05-24 00:00:00.000 |
| 2017-05-25 00:00:00.000 |
| 2017-05-26 00:00:00.000 |
| 2017-05-27 00:00:00.000 |
| 2017-05-28 00:00:00.000 |
| 2017-05-29 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| installdate             |
|:------------------------|
| 2019-06-10 00:00:00.000 |
| 2019-03-11 00:00:00.000 |
| 2019-03-29 00:00:00.000 |
| 2019-05-06 00:00:00.000 |
| 2018-12-12 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2019-05-10 00:00:00.000 |
| 2019-05-26 00:00:00.000 |
| 2019-05-15 00:00:00.000 |
| 2019-02-05 00:00:00.000 |
| 2018-11-01 00:00:00.000 |
| 2019-05-16 00:00:00.000 |
| 2019-05-03 00:00:00.000 |
| 2018-04-20 00:00:00.000 |
| 2019-03-18 00:00:00.000 |
| 2018-04-24 00:00:00.000 |
| 2019-04-17 00:00:00.000 |
| 2019-04-24 00:00:00.000 |
| 2017-05-05 00:00:00.000 |
| 2019-06-16 00:00:00.000 |
| 2019-04-23 00:00:00.000 |
| 2018-07-30 00:00:00.000 |
| 2019-06-07 00:00:00.000 |
| 2018-11-29 00:00:00.000 |
| 2019-03-22 00:00:00.000 |
| 2018-11-30 00:00:00.000 |
| 2018-12-03 00:00:00.000 |
| 2019-03-19 00:00:00.000 |
| 2019-05-12 00:00:00.000 |
| 2018-07-24 00:00:00.000 |
| 2019-06-17 00:00:00.000 |
| 2017-12-04 00:00:00.000 |
| 2018-09-12 00:00:00.000 |
| 2017-08-02 00:00:00.000 |
| 2019-03-26 00:00:00.000 |
| 2018-01-10 00:00:00.000 |
| 2019-04-02 00:00:00.000 |
| 2019-03-08 00:00:00.000 |
| 2019-06-11 00:00:00.000 |
| 2018-09-14 00:00:00.000 |
| 2019-05-19 00:00:00.000 |
| 2019-05-22 00:00:00.000 |
| 2019-06-13 00:00:00.000 |
| 2019-05-28 00:00:00.000 |
| 2019-02-23 00:00:00.000 |
| 2018-11-19 00:00:00.000 |
| 2018-04-13 00:00:00.000 |
| 2019-02-25 00:00:00.000 |
| 2019-04-29 00:00:00.000 |
| 2017-04-21 00:00:00.000 |
| 2019-04-16 00:00:00.000 |
| 2019-05-29 00:00:00.000 |
| 2017-06-26 00:00:00.000 |
| 2017-08-31 00:00:00.000 |
| 2019-05-01 00:00:00.000 |
| 2017-11-10 00:00:00.000 |
| 2019-03-04 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| spanishoccupation    |
|:---------------------|
| administrativo       |
| obrero especializado |
| profesional          |
| obrero               |
| gestión              |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| div_name                       |
|:-------------------------------|
| debit card department          |
| cyber issue dept               |
| insurance department           |
| e insurance department         |
| claims dept                    |
| claim department               |
| driver training                |
| mobile banking                 |
| e claims dept                  |
| e-banking issues               |
| forex department               |
| office of early learning       |
| state board of education       |
| public school claims dept      |
| professional standards board   |
| remit transactions             |
| atm transaction                |
| internet banking               |
| adult education and work force |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| week start date         |
|:------------------------|
| 2020-01-22 00:00:00.000 |
| 2020-01-26 00:00:00.000 |
| 2020-02-02 00:00:00.000 |
| 2020-02-09 00:00:00.000 |
| 2020-02-16 00:00:00.000 |
| 2020-02-23 00:00:00.000 |
| 2020-03-01 00:00:00.000 |
| 2020-03-08 00:00:00.000 |
| 2020-03-15 00:00:00.000 |
| 2020-03-22 00:00:00.000 |
| 2020-03-29 00:00:00.000 |
| 2020-04-05 00:00:00.000 |
| 2020-04-12 00:00:00.000 |
| 2020-04-19 00:00:00.000 |
| 2020-04-26 00:00:00.000 |
| 2020-05-03 00:00:00.000 |
| 2020-05-10 00:00:00.000 |
| 2020-05-17 00:00:00.000 |
| 2020-05-24 00:00:00.000 |
| 2020-05-31 00:00:00.000 |
| 2020-06-07 00:00:00.000 |
| 2020-06-14 00:00:00.000 |
| 2020-06-21 00:00:00.000 |
| 2020-06-28 00:00:00.000 |
| 2020-07-05 00:00:00.000 |
| 2020-07-12 00:00:00.000 |
| 2020-07-19 00:00:00.000 |
| 2020-07-26 00:00:00.000 |
| 2020-08-02 00:00:00.000 |
| 2020-08-09 00:00:00.000 |
| 2020-08-16 00:00:00.000 |
| 2020-08-23 00:00:00.000 |
| 2020-08-30 00:00:00.000 |
| 2020-09-06 00:00:00.000 |
| 2020-09-13 00:00:00.000 |
| 2020-09-20 00:00:00.000 |
| 2020-09-27 00:00:00.000 |
| 2020-10-04 00:00:00.000 |
| 2020-10-11 00:00:00.000 |
| 2020-10-18 00:00:00.000 |
| 2020-10-25 00:00:00.000 |
| 2020-11-01 00:00:00.000 |
| 2020-11-08 00:00:00.000 |
| 2020-11-15 00:00:00.000 |
| 2020-11-22 00:00:00.000 |
| 2020-11-29 00:00:00.000 |
| 2020-12-06 00:00:00.000 |
| 2020-12-13 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| name   |
|:-------|
| john   |
| helen  |
| jane   |
| brian  |