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:
| openeddatetime          |
|:------------------------|
| 2018-05-16 06:33:06.000 |
| 2017-06-21 13:00:29.000 |
| 2017-05-25 13:01:49.000 |
| 2017-05-25 13:01:53.000 |
| 2017-05-24 16:28:30.000 |
| 2018-04-27 12:22:01.000 |
| 2017-12-12 17:46:39.000 |
| 2018-03-05 15:38:47.000 |
| 2017-10-20 09:37:37.000 |
| 2018-03-15 12:08:34.000 |
| 2018-03-15 12:11:13.000 |
| 2018-05-15 14:28:51.000 |
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:
| streettype   |
|:-------------|
| street       |
| road         |
| lane         |
| avenue       |
| placeaza     |
| t-way        |
| pedestrain   |
| others       |
| way          |
| crescent     |
| highway      |
| terrace      |
| place        |
| close        |
| drive        |
| street n     |
| road w       |
| square       |


label: pedestrain
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:
| channel     |
|:------------|
| wholesale   |
| distributor |
| export      |
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:
| grupos_idade   |
|:---------------|
| 26 - 31        |
| 44 - 49        |
| 20 - 25        |
| 38 - 43        |
| 50 - 55        |
| 32 - 37        |
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:
| value                                       |
|:--------------------------------------------|
| nieuwe hollandse bos-variant                |
| nieuwe hollandse bos-variant_json           |
| west holland                                |
| west holland_json                           |
| delta 21                                    |
| delta 21_json                               |
| north european enclosure dam                |
| north european enclosure dam_json           |
| advies borm & huygens                       |
| advies borm & huygens_json                  |
| sluizen en duinen in de rijnmond            |
| sluizen en duinen in de rijnmond_json       |
| rijnmonding als estuarium                   |
| rijnmonding als estuarium_json              |
| een natuurlijke toekomsst voor nl 2120      |
| een natuurlijke toekomsst voor nl 2120_json |
| dubbele dijken in de delta                  |
| dubbele dijken in de delta_json             |
| nl 2300                                     |
| nl 2300_json                                |
| plan b 2220                                 |
| plan b 2220_json                            |
| new netherlands                             |
| new netherlands_json                        |
| kleirijperij                                |
| kleirijperij_json                           |
| kwelderontwikkeling marconi delfzijl        |
| kwelderontwikkeling marconi delfzijl_json   |
| _json                                       |
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:
| warehouse_block   |
|:------------------|
| f                 |
| d                 |
| c                 |
| a                 |
| b                 |
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:
| finish   |
|:---------|
| ss       |
| bzp      |
| sc       |
| white    |
| sss      |
| wh       |
| f        |
| scp      |
| saa      |
| sil      |
| bz       |
| ssf      |
| al       |
| sse      |
| sa       |
| blk      |
| pl       |
| steel    |
| pc       |
| 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:
| country (clusters)   |
|:---------------------|
| cluster3             |
| cluster1             |
| cluster2             |
| cluster4             |
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                   |
|:------------------------|
| 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 |
| 2015-12-01 00:00:00.000 |
| 2016-01-01 00:00:00.000 |
| 2016-02-01 00:00:00.000 |
| 2016-03-01 00:00:00.000 |
| 2016-04-01 00:00:00.000 |
| 2016-05-01 00:00:00.000 |
| 2016-06-01 00:00:00.000 |
| 2016-07-01 00:00:00.000 |
| 2016-08-01 00:00:00.000 |
| 2016-09-01 00:00:00.000 |
| 2016-10-01 00:00:00.000 |
| 2016-11-01 00:00:00.000 |
| 2016-12-01 00:00:00.000 |
| 2017-01-01 00:00:00.000 |
| 2017-02-01 00:00:00.000 |
| 2017-03-01 00:00:00.000 |
| 2017-04-01 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:
| native-country             |
|:---------------------------|
| united-states              |
| mexico                     |
| ?                          |
| columbia                   |
| iran                       |
| france                     |
| guatemala                  |
| scotland                   |
| canada                     |
| greece                     |
| germany                    |
| ireland                    |
| peru                       |
| ecuador                    |
| poland                     |
| puerto-rico                |
| cuba                       |
| england                    |
| el-salvador                |
| philippines                |
| italy                      |
| portugal                   |
| honduras                   |
| outlying-us(guam-usvi-etc) |
| yugoslavia                 |
| nicaragua                  |
| dominican-republic         |
| thailand                   |
| japan                      |
| jamaica                    |
| hungary                    |
| india                      |
| hong                       |
| south                      |
| china                      |
| taiwan                     |
| cambodia                   |
| vietnam                    |
| trinadad&tobago            |
| haiti                      |
| laos                       |
| holand-netherlands         |


label: ?
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:
| дата сессии             |
|:------------------------|
| 2017-07-05 00:00:00.000 |
| 2017-07-07 00:00:00.000 |
| 2017-07-09 00:00:00.000 |
| 2017-07-10 00:00:00.000 |
| 2017-07-13 00:00:00.000 |
| 2017-07-15 00:00:00.000 |
| 2017-07-19 00:00:00.000 |
| 2017-07-20 00:00:00.000 |
| 2017-07-21 00:00:00.000 |
| 2017-07-22 00:00:00.000 |
| 2017-07-24 00:00:00.000 |
| 2017-07-26 00:00:00.000 |
| 2017-07-27 00:00:00.000 |
| 2017-07-28 00:00:00.000 |
| 2017-07-29 00:00:00.000 |
| 2017-07-31 00:00:00.000 |
| 2017-08-01 00:00:00.000 |
| 2017-08-02 00:00:00.000 |
| 2017-08-03 00:00:00.000 |
| 2017-08-06 00:00:00.000 |
| 2017-08-07 00:00:00.000 |
| 2017-08-09 00:00:00.000 |
| 2017-08-10 00:00:00.000 |
| 2017-08-11 00:00:00.000 |
| 2017-08-13 00:00:00.000 |
| 2017-08-14 00:00:00.000 |
| 2017-08-15 00:00:00.000 |
| 2017-08-18 00:00:00.000 |
| 2017-08-20 00:00:00.000 |
| 2017-08-24 00:00:00.000 |
| 2017-08-26 00:00:00.000 |
| 2017-08-29 00:00:00.000 |
| 2017-08-30 00:00:00.000 |
| 2017-08-31 00:00:00.000 |
| 2017-09-01 00:00:00.000 |
| 2017-09-04 00:00:00.000 |
| 2017-09-05 00:00:00.000 |
| 2017-09-06 00:00:00.000 |
| 2017-09-07 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:
| date                    |
|:------------------------|
| 2020-05-05 00:00:00.000 |
| 2020-05-06 00:00:00.000 |
| 2020-05-08 00:00:00.000 |
| 2020-05-09 00:00:00.000 |
| 2020-05-10 00:00:00.000 |
| 2020-05-13 00:00:00.000 |
| 2020-05-14 00:00:00.000 |
| 2020-05-15 00:00:00.000 |
| 2020-05-17 00:00:00.000 |
| 2020-05-18 00:00:00.000 |
| 2020-05-20 00:00:00.000 |
| 2020-05-21 00:00:00.000 |
| 2020-05-23 00:00:00.000 |
| 2020-05-24 00:00:00.000 |
| 2020-05-26 00:00:00.000 |
| 2020-05-30 00:00:00.000 |
| 2020-05-31 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2020-05-04 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:
| scope   |
|:--------|
| scopea  |
| scopeb  |
| scopec  |
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:
| mês      |
|:---------|
| março    |
| abril    |
| maio     |
| junho    |
| julho    |
| agosto   |
| setembro |
| outubro  |
| novembro |
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:
| nadb_riskratingmethodologyid         |
|:-------------------------------------|
| 06c0009c-c154-ea11-a813-000d3a4e6589 |
| 08c0009c-c154-ea11-a813-000d3a4e6589 |
| 0ac0009c-c154-ea11-a813-000d3a4e6589 |
| 0cc0009c-c154-ea11-a813-000d3a4e6589 |
| 16c0009c-c154-ea11-a813-000d3a4e6589 |
| 14c0009c-c154-ea11-a813-000d3a4e6589 |
| 04c0009c-c154-ea11-a813-000d3a4e6589 |
| 10c0009c-c154-ea11-a813-000d3a4e6589 |
| 0ec0009c-c154-ea11-a813-000d3a4e6589 |
| 12c0009c-c154-ea11-a813-000d3a4e6589 |
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:
| end date                |
|:------------------------|
| 2014-09-19 00:00:00.000 |
| 2015-03-19 00:00:00.000 |
| 2015-09-19 00:00:00.000 |
| 2016-03-19 00:00:00.000 |
| 2016-09-19 00:00:00.000 |
| 2017-03-19 00:00:00.000 |
| 2017-09-19 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:
| report_time             |
|:------------------------|
| 2021-07-20 11:16:00.000 |
| 2021-07-20 12:05:00.000 |
| 2021-07-20 13:05:00.000 |
| 2021-07-20 14:05:00.000 |
| 2021-07-20 15:05:00.000 |
| 2021-07-20 16:05:00.000 |
| 2021-07-20 17:05:00.000 |
| 2021-07-20 18:05:00.000 |
| 2021-07-20 19:05:00.000 |
| 2021-07-20 20:05:00.000 |
| 2021-07-20 21:05:00.000 |
| 2021-07-20 22:05:00.000 |
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:
| level id   |
|:-----------|
| bm00       |
| gf101      |
| f2.0       |
| f3.0       |
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:
| colour           |
|:-----------------|
| auburn           |
| black            |
| black/blue       |
| blond            |
| blue             |
| blue/white       |
| brown            |
| brown/black      |
| brown/white      |
| gold             |
| grey             |
| green            |
| green/blue       |
| hazel            |
| indigo           |
| magenta          |
| orange           |
| orange/white     |
| pink             |
| purple           |
| red              |
| red/black        |
| red/grey         |
| red/orange       |
| red/white        |
| silver           |
| strawberry blond |
| violet           |
| white            |
| white/red        |
| yellow           |
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:
| entitytype   |
|:-------------|
| conference   |
| author       |
| affiliation  |
| journal      |
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:
| remaining time (days)   |
|:------------------------|
| 2-7 days                |
| 0-1 days                |
| 8-15 days               |
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:
| quarters   |
|:-----------|
| q3         |
| q4         |
| q2         |
| q1         |
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:
| holidays.holiday                |
|:--------------------------------|
| deň vzniku slovenskej republiky |
| zjavenie pána (traja králi)     |
| veľký piatok                    |
| veľkonočný pondelok             |
| sviatok práce                   |
| deň víťazstva nad fašizmom      |
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:
| z_location   |
|:-------------|
| city-se      |
| city-us      |
| city-in      |
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:
| region   |
|:---------|
| mena     |
| asia     |
| europe   |
| gcc      |
| uk       |
| usa      |
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:
| power_steering                  |
|:--------------------------------|
| electric power                  |
| electric power, hydraulic power |
| hydraulic power                 |
| electro-hydraulic               |
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 & year   |
|:---------------|
| feb 2020       |
| mar 2020       |
| apr 2020       |
| may 2020       |
| jun 2020       |
| jul 2020       |
| aug 2020       |
| sep 2020       |
| oct 2020       |
| nov 2020       |
| dec 2020       |
| jan 2021       |
| feb 2021       |
| mar 2021       |
| apr 2021       |
| may 2021       |
| jun 2021       |
| jul 2021       |
| aug 2021       |
| sep 2021       |
| oct 2021       |
| nov 2021       |
| dec 2021       |
| jan 2022       |
| feb 2022       |
| mar 2022       |
| apr 2022       |
| may 2022       |
| jun 2022       |
| jul 2022       |
| aug 2022       |
| sep 2022       |
| oct 2022       |
| nov 2022       |
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              |
|:------------------|
| username          |
| encryptedpassword |
| cards             |
| customer          |
| dispatcher        |
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                        |
|:------------------------------|
| woman                         |
| man                           |
| other or prefer not to answer |
| non-binary                    |
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:
| purchase order date     |
|:------------------------|
| 2020-11-04 00:00:00.000 |
| 2020-12-01 00:00:00.000 |
| 2021-02-24 00:00:00.000 |
| 2021-07-12 00:00:00.000 |
| 2020-12-07 00:00:00.000 |
| 2020-12-15 00:00:00.000 |
| 2021-05-16 00:00:00.000 |
| 2020-09-21 00:00:00.000 |
| 2020-10-02 00:00:00.000 |
| 2020-11-02 00:00:00.000 |
| 2020-11-21 00:00:00.000 |
| 2021-01-22 00:00:00.000 |
| 2021-02-07 00:00:00.000 |
| 2021-03-16 00:00:00.000 |
| 2021-04-04 00:00:00.000 |
| 2021-04-21 00:00:00.000 |
| 2021-04-27 00:00:00.000 |
| 2021-05-10 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:
| endofmonth              |
|:------------------------|
| 2020-01-31 00:00:00.000 |
| 2020-02-29 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-04-30 00:00:00.000 |
| 2020-05-31 00:00:00.000 |
| 2020-06-30 00:00:00.000 |
| 2020-07-31 00:00:00.000 |
| 2020-08-31 00:00:00.000 |
| 2020-09-30 00:00:00.000 |
| 2020-10-31 00:00:00.000 |
| 2020-11-30 00:00:00.000 |
| 2020-12-31 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 |
| 2021-08-31 00:00:00.000 |
| 2021-09-30 00:00:00.000 |
| 2021-10-31 00:00:00.000 |
| 2021-11-30 00:00:00.000 |
| 2021-12-31 00:00:00.000 |
| 2022-01-31 00:00:00.000 |
| 2022-02-28 00:00:00.000 |
| 2022-03-31 00:00:00.000 |
| 2022-04-30 00:00:00.000 |
| 2022-05-31 00:00:00.000 |
| 2022-06-30 00:00:00.000 |
| 2022-07-31 00:00:00.000 |
| 2022-08-31 00:00:00.000 |
| 2022-09-30 00:00:00.000 |
| 2022-10-31 00:00:00.000 |
| 2022-11-30 00:00:00.000 |
| 2022-12-31 00:00:00.000 |
| 2023-01-31 00:00:00.000 |
| 2023-02-28 00:00:00.000 |
| 2023-03-31 00:00:00.000 |
| 2023-04-30 00:00:00.000 |
| 2023-05-31 00:00:00.000 |
| 2023-06-30 00:00:00.000 |
| 2023-07-31 00:00:00.000 |
| 2023-08-31 00:00:00.000 |
| 2023-09-30 00:00:00.000 |
| 2023-10-31 00:00:00.000 |
| 2023-11-30 00:00:00.000 |
| 2023-12-31 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:
| date                    |
|:------------------------|
| 2020-01-03 00:00:00.000 |
| 2020-01-04 00:00:00.000 |
| 2020-01-05 00:00:00.000 |
| 2020-01-06 00:00:00.000 |
| 2020-01-07 00:00:00.000 |
| 2020-01-08 00:00:00.000 |
| 2020-01-09 00:00:00.000 |
| 2020-01-10 00:00:00.000 |
| 2020-01-11 00:00:00.000 |
| 2020-01-12 00:00:00.000 |
| 2020-01-13 00:00:00.000 |
| 2020-01-14 00:00:00.000 |
| 2020-01-15 00:00:00.000 |
| 2020-01-16 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:
| fy q in fy   |
|:-------------|
| q1 fy08      |
| q1 fy09      |
| q1 fy10      |
| q1 fy11      |
| q1 fy12      |
| q1 fy14      |
| q1 fy15      |
| q1 fy16      |
| q1 fy17      |
| q1 fy18      |
| q1 fy19      |
| q1 fy20      |
| q1 fy21      |
| q1 fy22      |
| q1 fy23      |
| q1 fy24      |
| q1 fy25      |
| q1 fy13      |
| q4 fy08      |
| q4 fy09      |
| q4 fy10      |
| q4 fy11      |
| q4 fy12      |
| q4 fy13      |
| q4 fy14      |
| q4 fy15      |
| q4 fy16      |
| q4 fy17      |
| q4 fy18      |
| q4 fy19      |
| q4 fy20      |
| q4 fy21      |
| q4 fy22      |
| q4 fy23      |
| q4 fy24      |
| q4 fy25      |
| q3 fy08      |
| q3 fy09      |
| q3 fy10      |
| q3 fy11      |
| q3 fy12      |
| q3 fy13      |
| q3 fy14      |
| q3 fy15      |
| q3 fy16      |
| q3 fy17      |
| q3 fy18      |
| q3 fy19      |
| q3 fy20      |
| q3 fy21      |
| q3 fy22      |
| q3 fy23      |
| q3 fy24      |
| q3 fy25      |
| q2 fy09      |
| q2 fy10      |
| q2 fy11      |
| q2 fy12      |
| q2 fy14      |
| q2 fy15      |
| q2 fy16      |
| q2 fy17      |
| q2 fy18      |
| q2 fy20      |
| q2 fy21      |
| q2 fy22      |
| q2 fy23      |
| q2 fy24      |
| q2 fy25      |
| q2 fy08      |
| q2 fy13      |
| q2 fy19      |
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:
| language      |
|:--------------|
| c#            |
| php           |
| android       |
| jquery        |
| python        |
| html          |
| c++           |
| ios           |
| mysql         |
| css           |
| sql           |
| asp.net       |
| objective-c   |
| .net          |
| iphone        |
| ruby-on-rails |
| c             |
| ruby          |
| arrays        |
| sql-server    |
| ajax          |
| regex         |
| xml           |
| json          |
| asp.net-mvc   |
| wpf           |
| linux         |
| django        |
| database      |
| angularjs     |
| r             |
| eclipse       |
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:
| fuel_type    |
|:-------------|
| diesel       |
| petrol       |
| electric     |
| hybrid       |
| cng          |
| cng + petrol |
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 classification      |
|:-------------------------|
| low business impact      |
| moderate business impact |
| high business impact     |
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             |
|:-----------------|
| program          |
| purpose          |
| repeat frequency |
| survey strategy  |
| guideline method |