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:
| dept   |
|:-------|
| mus    |
| cs     |
| lti    |
| rob    |
| hci    |
| se     |
| etc    |
| isr    |
| msc    |
| epp    |
| che    |
| cit    |
| ece    |
| mse    |
| sv     |
| eng    |
| ml     |
| psy    |
| his    |
| phi    |
| hss    |
| sta    |
| des    |
| art    |
| cfa    |
| dra    |
| cb     |
| mlg    |
| phy    |
| bsc    |
| cmy    |
| bmd    |
| cee    |
| ips    |
| cnb    |
| sds    |
| arc    |
| ba     |
| ict    |
| meg    |
| h00    |
| ish    |
| ini    |
| eco    |
| cas    |
| wc     |
| mcs    |
| ia     |
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:
| segment     |
|:------------|
| home office |
| corporate   |
| consumer    |
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:
| developmentplan           |
|:--------------------------|
| 2018 emp development plan |
| 2019 emp development plan |
| 2020 emp development plan |
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:
| entityhourrangeidunique                                                       |
|:------------------------------------------------------------------------------|
| http://dev.demo.appdynamics.com/app=6/node=1764331/date=8/4/2020/hour=1:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764331/date=8/4/2020/hour=2:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764330/date=8/4/2020/hour=1:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764330/date=8/4/2020/hour=2:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764328/date=8/4/2020/hour=1:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764328/date=8/4/2020/hour=2:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764332/date=8/4/2020/hour=1:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=1764332/date=8/4/2020/hour=2:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=2183313/date=8/4/2020/hour=1:00 pm |
| http://dev.demo.appdynamics.com/app=6/node=2183313/date=8/4/2020/hour=2:00 pm |
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:
| gender   |
|:---------|
| male     |
| female   |
| mixed    |
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:
| data                    |
|:------------------------|
| 2021-03-01 00:00:00.000 |
| 2021-04-01 00:00:00.000 |
| 2021-05-01 00:00:00.000 |
| 2021-06-01 00:00:00.000 |
| 2021-07-01 00:00:00.000 |
| 2021-08-01 00:00:00.000 |
| 2021-01-31 00:00:00.000 |
| 2021-02-28 00:00:00.000 |
| 2021-03-31 00:00:00.000 |
| 2021-04-30 00:00:00.000 |
| 2021-05-31 00:00:00.000 |
| 2021-06-30 00:00:00.000 |
| 2021-07-31 00:00:00.000 |
| 2021-01-15 00:00:00.000 |
| 2021-02-14 00:00:00.000 |
| 2021-03-15 00:00:00.000 |
| 2021-04-15 00:00:00.000 |
| 2021-05-15 00:00:00.000 |
| 2021-06-15 00:00:00.000 |
| 2021-07-15 00:00:00.000 |
| 2021-08-15 00:00:00.000 |
| 2021-08-31 00:00:00.000 |
| 2021-09-01 00:00:00.000 |
| 2021-09-15 00:00:00.000 |
| 2021-09-30 00:00:00.000 |
| 2021-10-01 00:00:00.000 |
| 2021-10-15 00:00:00.000 |
| 2021-10-30 00:00:00.000 |
| 2021-11-01 00:00:00.000 |
| 2021-11-15 00:00:00.000 |
| 2021-11-30 00:00:00.000 |
| 2021-12-01 00:00:00.000 |
| 2021-12-15 00:00:00.000 |
| 2021-12-31 00:00:00.000 |
| 2022-01-01 00:00:00.000 |
| 2022-01-15 00:00:00.000 |
| 2022-01-31 00:00:00.000 |
| 2022-02-01 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:
| state (1790)   |
|:---------------|
| maine          |
| massachusetts  |
| rhode island   |
| conneticut     |
| new york       |
| new jersey     |
| pennsylvania   |
| delaware       |
| maryland       |
| virginia       |
| kentucky       |
| north carolina |
| south carolina |
| georgia        |


label: conneticut
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:
| manufacturername   |
|:-------------------|
| vertex             |
| omega              |
| avzilla            |
| beacon             |
| eagle              |
| concord            |
| mind               |
| impluse            |
| climax             |
| apple              |


label: impluse
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:
| calendar startofquarter   |
|:--------------------------|
| 2018-01-01 00:00:00.000   |
| 2018-04-01 00:00:00.000   |
| 2018-07-01 00:00:00.000   |
| 2018-10-01 00:00:00.000   |
| 2019-01-01 00:00:00.000   |
| 2019-04-01 00:00:00.000   |
| 2019-07-01 00:00:00.000   |
| 2019-10-01 00:00:00.000   |
| 2020-01-01 00:00:00.000   |
| 2020-04-01 00:00:00.000   |
| 2020-07-01 00:00:00.000   |
| 2020-10-01 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:
| table2.cl2    |
|:--------------|
| 3.ewrwerew    |
| 4.werqwer     |
| 5.werwqrewrew |
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 time               |
|:------------------------|
| 2022-02-03 00:00:00.000 |
| 2022-02-01 00:00:00.000 |
| 2022-02-02 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:
| continent     |
|:--------------|
| asia          |
| europe        |
| africa        |
| north america |
| south america |
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    |
|:---------|
| january  |
| february |
| march    |
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:
| carrier                                                                             |
|:------------------------------------------------------------------------------------|
| expressjet airlines inc.                                                            |
| skywest airlines inc.                                                               |
| american eagle airlines inc.                                                        |
| delta air lines inc.                                                                |
| american airlines inc.                                                              |
| united air lines inc.                                                               |
| southwest airlines co.                                                              |
| us airways inc. (merged with america west 9/05. reporting for both starting 10/07.) |
| jetblue airways                                                                     |
| alaska airlines inc.                                                                |
| frontier airlines inc.                                                              |
| airtran airways corporation                                                         |
| hawaiian airlines inc.                                                              |
| virgin america                                                                      |
| mesa airlines inc.                                                                  |
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:
| category                  |
|:--------------------------|
| fel antal mellan p och r. |
| fel antal i både p och r. |
| omkonst.                  |
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:
| country   |
|:----------|
| germany   |
| mexico    |
| canada    |
| france    |
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:
| continent name   |
|:-----------------|
| north america    |
| asia             |
| africa           |
| oceania          |
| europe           |
| antartica        |
| south america    |


label: antartica
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:
| vùng   |
|:-------|
| r1     |
| r2     |
| r3     |
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:
| weekended   |
|:------------|
| 3/7/2020    |
| 3/14/2020   |
| 3/21/2020   |
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:
| period                  |
|:------------------------|
| 2015-03-01 00:00:00.000 |
| 2015-04-01 00:00:00.000 |
| 2015-05-01 00:00:00.000 |
| 2015-06-01 00:00:00.000 |
| 2015-07-01 00:00:00.000 |
| 2015-08-01 00:00:00.000 |
| 2015-09-01 00:00:00.000 |
| 2015-10-01 00:00:00.000 |
| 2015-11-01 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:
| ship mode      |
|:---------------|
| standard class |
| second class   |
| first class    |
| same day       |
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:
| segmentation   |
|:---------------|
| bronze         |
| national       |
| new            |
| gold           |
| silver         |
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:
| ubicación              |
|:-----------------------|
| surco,lima,lima        |
| ate,lima,lima          |
| san miguel, lima, lima |
| san isidro, lima, lima |
| la molina,lima, lima   |
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:
| time                    |
|:------------------------|
| 1899-12-30 07:57:00.000 |
| 1899-12-30 08:07:00.000 |
| 1899-12-30 08:55:00.000 |
| 1899-12-30 08:58:00.000 |
| 1899-12-30 09:02:00.000 |
| 1899-12-30 09:05:00.000 |
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:
| vin   |
|:------|
| a1    |
| a2    |
| a3    |
| a4    |
| a5    |
| a6    |
| a7    |
| a8    |
| a9    |
| a10   |
| a11   |
| a12   |
| a13   |
| a14   |
| a15   |
| a16   |
| a17   |
| a18   |
| a19   |
| a20   |
| a21   |
| a22   |
| a23   |
| a24   |
| a25   |
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:
| rainlevel24hrfk   |
|:------------------|
| 24hr1             |
| 24hr0             |
| 24hr2             |
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:
| sala    |
|:--------|
| piso    |
| guardia |
| uti     |
| neo     |
| uco     |
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:
| ddl   |
|:------|
| edd   |
| sdd   |
| rdd   |
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:
| yearmonthnumber   |
|:------------------|
| 2020/07           |
| 2020/08           |
| 2020/09           |
| 2020/01           |
| 2020/02           |
| 2020/03           |
| 2020/04           |
| 2020/05           |
| 2020/06           |
| 2020/10           |
| 2020/11           |
| 2020/12           |
| 2019/01           |
| 2019/02           |
| 2019/03           |
| 2019/10           |
| 2019/11           |
| 2019/12           |
| 2019/04           |
| 2019/05           |
| 2019/06           |
| 2019/07           |
| 2019/08           |
| 2019/09           |
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:
| payroll area (description)   |
|:-----------------------------|
| monthly                      |
| non-payroll-relevant         |
| markem-imaje france          |
| sweden                       |
| markem-imaje germany         |
| monthly  - gb                |
| not assigned                 |
| markem-imaje portuga         |
| markem-imaje brazil          |
| hr-cn: monthly               |
| hr-in : monthly              |
| hr-mx: fortnightly           |
| markem-imajemalaysia         |
| markem-imaje singapo         |
| markem-imaje us              |
| markem-imaje belgium         |
| markem-imaje italy           |
| monthly - nederland          |
| hr-d: sal. employees         |
| markem-imaje spain           |
| hr-ar:mensual                |
| markem-imaje swit.           |
| markem-majeaustralia         |
| markem-imaje denmark         |
| markem-imaje finland         |
| markem-imaje canada          |
| hr-th: monthly               |
| markem-imaje austria         |
| hr-tw: monthly               |
| monthly-wage earners         |
| markem-imaje russia          |
| markem-imaje japan           |
| monthly        - nz          |
| markem-imaje ireland         |
| markem-imaje norway          |
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            |
|:------------------------|
| 2022-01-13 02:26:53.746 |
| 2022-02-27 20:32:42.206 |
| 2022-02-27 20:32:31.910 |
| 2022-02-27 20:32:23.963 |
| 2022-02-27 20:32:44.420 |
| 2022-02-27 20:32:34.930 |
| 2022-02-09 15:23:09.826 |
| 2022-02-27 20:32:47.326 |
| 2022-02-27 20:32:39.293 |
| 2022-01-25 15:28:39.816 |
| 2022-02-27 20:44:51.360 |
| 2022-02-06 17:40:10.300 |
| 2022-01-23 22:42:08.540 |
| 2022-01-23 17:01:18.123 |
| 2022-01-22 16:22:29.693 |
| 2022-01-22 16:24:19.870 |
| 2022-02-03 22:16:40.553 |
| 2022-02-08 13:58:19.983 |
| 2022-01-13 02:29:20.833 |
| 2022-01-23 17:35:40.776 |
| 2022-01-31 13:42:13.596 |
| 2022-01-22 16:16:15.863 |
| 2022-01-25 17:27:19.350 |
| 2022-02-14 23:58:28.263 |
| 2022-01-13 02:27:00.186 |
| 2022-01-13 02:27:00.286 |
| 2022-01-13 02:27:00.296 |
| 2022-01-13 02:27:00.283 |
| 2022-01-13 02:27:00.280 |
| 2022-01-13 02:29:20.846 |
| 2022-02-03 22:11:44.816 |
| 2022-02-09 15:23:11.690 |
| 2022-01-14 22:11:11.890 |
| 2022-01-13 13:52:25.606 |
| 2022-01-13 13:52:22.510 |
| 2022-01-13 13:52:29.686 |
| 2022-01-23 17:29:15.673 |
| 2022-01-23 17:29:01.640 |
| 2022-01-23 18:27:36.383 |
| 2022-01-25 15:41:25.093 |
| 2022-01-25 15:27:35.413 |
| 2022-01-25 15:41:54.433 |
| 2022-01-25 15:42:14.410 |
| 2022-01-25 15:41:47.370 |
| 2022-01-25 15:40:56.756 |
| 2022-01-25 15:42:29.383 |
| 2022-01-25 15:42:35.616 |
| 2022-01-25 15:42:42.013 |
| 2022-02-15 14:13:23.116 |
| 2022-02-15 14:13:15.693 |
| 2022-02-08 14:17:25.293 |
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:
| category        |
|:----------------|
| office supplies |
| furniture       |
| technology      |
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           |
|:---------------|
| solo           |
| formal group   |
| informal group |
| studio         |
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:
| having completed courses   |
|:---------------------------|
| yes                        |
| no                         |
| neutral                    |
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:
| vendorinformation.vendor   |
|:---------------------------|
| palo alto networks         |
| microsoft                  |
| symantec                   |
| contrast security          |
| illumio                    |
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:
| participant_code   |
|:-------------------|
| melk               |
| euct               |
| collgar            |
| korl               |
| pjrh               |
| mund               |
| pug                |
| wgutd              |
| gw                 |
| mcg                |
| tsla_mgt           |
| trmos              |
| pmc                |
| sthrncrs           |
| auricon            |
| auxc               |
| dnhr               |
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:
| uf   |
|:-----|
| al   |
| ap   |
| ba   |
| ce   |
| df   |
| es   |
| go   |
| mg   |
| pa   |
| pb   |
| pe   |
| pi   |
| pr   |
| rj   |
| rn   |
| ro   |
| rs   |
| sc   |
| se   |
| sp   |
| ac   |
| rr   |
| ma   |
| ms   |
| mt   |
| am   |
| to   |
| ni   |
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:
| sites     |
|:----------|
| location2 |
| location1 |
| location3 |
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:
| stateprovincecode   |
|:--------------------|
| mb                  |
| nb                  |
| pe                  |
| hi                  |
| id                  |
| nm                  |
| pa                  |
| tn                  |
| tx                  |
| va                  |
| nd                  |
| nj                  |
| ut                  |
| in                  |
| nv                  |
| qc                  |
| fl                  |
| bc                  |
| al                  |
| ms                  |
| ma                  |
| mn                  |
| ny                  |
| ok                  |
| az                  |
| il                  |
| sd                  |
| vt                  |
| de                  |
| la                  |
| ab                  |
| dc                  |
| ia                  |
| ak                  |
| md                  |
| me                  |
| mi                  |
| mt                  |
| nc                  |
| wv                  |
| ca                  |
| sk                  |
| oh                  |
| or                  |
| ri                  |
| ga                  |
| ar                  |
| ct                  |
| nh                  |
| wa                  |
| mo                  |
| on                  |
| co                  |
| wi                  |
| sc                  |
| ne                  |
| ky                  |
| ns                  |
| ks                  |