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:
| industry type                     |
|:----------------------------------|
| supermarket                       |
| superstore                        |
| departmental store                |
| store / retail unit               |
| minimarket                        |
| newsagent / off-licence           |
| hairdresser                       |
| outdoor / stalls market           |
| station / depot / stop            |
| hotel / motel / b&b               |
| holiday park / camp site          |
| cinema / theatre                  |
| pub with no food                  |
| restaurant / pub with food        |
| fast food / diner                 |
| take away                         |
| café                              |
| sports club / function room       |
| industrial plant area             |
| logistics / warehouse / dc        |
| food manufacture                  |
| textile manufacture               |
| other manufacture                 |
| precision engineering             |
| mechanical engineering            |
| other engineering                 |
| construction / demolition         |
| nursery / day care                |
| primary school                    |
| secondary school                  |
| college                           |
| university                        |
| instructor / sport academy        |
| small office / agency             |
| big office                        |
| leisure centre / gym              |
| park / cemetery                   |
| student accommodation             |
| domestic apartments               |
| hospital                          |
| care home                         |
| surgery / dentist                 |
| vet                               |
| vehicle sales forecourt           |
| petrol station                    |
| mechanics garage                  |
| waste broker                      |
| national waste management company |
| post office                       |
| church / religious organisation   |
| washing and dry-cleaning          |
| funeral & cemetery activities     |
| spa / solarium / massage / beauty |
| pet services                      |
| tattoist                          |
| night club                        |
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   |
|:-----|
| fy1  |
| fy2  |
| fy3  |
| fy4  |
| fy5  |
| fy6  |
| fy7  |
| fy8  |
| fy9  |
| fy10 |
| fy11 |
| fy12 |
| fy13 |
| fy14 |
| fy15 |
| fy16 |
| fy17 |
| fy18 |
| fy19 |
| fy20 |
| fy21 |
| fy00 |
| fy01 |
| fy02 |
| fy03 |
| fy04 |
| fy05 |
| fy06 |
| fy07 |
| fy08 |
| fy09 |
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:
| 중분류            |
|:------------------|
| helmets           |
| caps              |
| forks             |
| headsets          |
| handlebars        |
| wheels            |
| panniers          |
| locks             |
| pumps             |
| lights            |
| bottles and cages |
| tires and tubes   |
| bike racks        |
| cleaners          |
| fenders           |
| bike stands       |
| derailleurs       |
| brakes            |
| saddles           |
| pedals            |
| cranksets         |
| chains            |
| bottom brackets   |
| road bikes        |
| road frames       |
| shorts            |
| tights            |
| bib-shorts        |
| hydration packs   |
| mountain frames   |
| mountain bikes    |
| socks             |
| jerseys           |
| vests             |
| touring frames    |
| touring bikes     |
| gloves            |
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_1   |
|:---------|
| bug      |
| normal   |
| ground   |
| fire     |
| poison   |
| grass    |
| ghost    |
| electric |
| water    |
| ice      |
| psychic  |
| dark     |
| rock     |
| fighting |
| steel    |
| dragon   |
| fairy    |
| flying   |
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:
| uo   |
|:-----|
| has  |
| har  |
| bzn  |
| hre  |
| opb  |
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:
| debitorcity   |
|:--------------|
| münchen       |
| birmingham    |
| vienna        |
| s             |
| london        |


label: s
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:
| appl_date               |
|:------------------------|
| 2020-02-28 00:00:00.000 |
| 2020-01-28 00:00:00.000 |
| 2019-09-28 00:00:00.000 |
| 2019-01-28 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:
| agent   |
|:--------|
| dan     |
| joe     |
| becky   |
| stewart |
| martha  |
| jim     |
| diane   |
| greg    |
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:
| year                    |
|:------------------------|
| 2018-01-01 00:00:00.000 |
| 2017-01-01 00:00:00.000 |
| 2016-01-01 00:00:00.000 |
| 2015-01-01 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:
| country              |
|:---------------------|
| denmark              |
| netherlands          |
| germany              |
| ireland              |
| united states        |
| canada               |
| new zealand          |
| singapore            |
| hong kong            |
| liechtenstein        |
| sweden               |
| united kingdom       |
| iceland              |
| south korea          |
| israel               |
| luxembourg           |
| japan                |
| belgium              |
| france               |
| austria              |
| finland              |
| slovenia             |
| spain                |
| italy                |
| czech republic       |
| greece               |
| estonia              |
| brunei               |
| cyprus               |
| qatar                |
| andorra              |
| slovakia             |
| poland               |
| lithuania            |
| malta                |
| saudi arabia         |
| argentina            |
| united arab emirates |
| chile                |
| portugal             |
| hungary              |
| bahrain              |
| latvia               |
| croatia              |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| start of hour           |
|:------------------------|
| 1899-12-30 17:00:00.000 |
| 1899-12-30 16:00:00.000 |
| 1899-12-30 19:00:00.000 |
| 1899-12-30 21:00:00.000 |
| 1899-12-30 23:00:00.000 |
| 1899-12-30 01:00:00.000 |
| 1899-12-30 02:00:00.000 |
| 1899-12-30 06:00:00.000 |
| 1899-12-30 08:00:00.000 |
| 1899-12-30 11:00:00.000 |
| 1899-12-30 09:00:00.000 |
| 1899-12-30 18:00:00.000 |
| 1899-12-30 20:00:00.000 |
| 1899-12-30 07:00:00.000 |
| 1899-12-30 10:00:00.000 |
| 1899-12-30 12:00:00.000 |
| 1899-12-30 13:00:00.000 |
| 1899-12-30 15:00:00.000 |
| 1899-12-30 00:00:00.000 |
| 1899-12-30 14:00:00.000 |
| 1899-12-30 03:00:00.000 |
| 1899-12-30 22:00:00.000 |
| 1899-12-30 04:00:00.000 |
| 1899-12-30 05:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| code       |
|:-----------|
| 18+r5%     |
| 18+rc2     |
| 18+rc3     |
| 18+rcx     |
| 32bc++     |
| c++ktruong |
| c4tonl     |
| covid_c3   |
| covid_xcar |
| hb_10%     |
| hb_15%     |
| hb_20%     |
| hdt10%     |
| on-20%     |
| onl10%     |
| onl15%     |
| onl3.2     |
| onl38%     |
| open12t    |
| open24t    |
| opens04    |
| opensp     |
| pmi15%     |
| ref05%     |
| ref10%     |
| ref15%     |
| spe_t2     |
| spe20%     |
| summer2020 |
| trian12t   |
| trian24t   |
| triansp    |
| voucher500 |
| xcart4     |
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:
| group   |
|:--------|
| blue    |
| green   |
| 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:
| garagecond   |
|:-------------|
| ta           |
| 0            |
| gd           |
| po           |
| fa           |
| ex           |
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:
| empjobrole                |
|:--------------------------|
| senior developer          |
| developer                 |
| sales executive           |
| data scientist            |
| sales representative      |
| laboratory technician     |
| manager r&d               |
| research director         |
| research scientist        |
| manager                   |
| finance manager           |
| manufacturing director    |
| human resources           |
| technical architect       |
| business analyst          |
| technical lead            |
| delivery manager          |
| healthcare representative |
| senior manager r&d        |
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   |
|:-----------|
| b          |
| a          |
| c          |
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:
| diretoria      |
|:---------------|
| financeiro     |
| administrativo |
| industrial     |
| suprimentos    |
| vendas         |
| informática    |
| marketing      |
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:
| diacorto   |
|:-----------|
| mié        |
| jue        |
| vie        |
| lun        |
| mar        |
| dom        |
| sáb        |
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:
| dim student.dim date - school year   |
|:-------------------------------------|
| 2007-07-01 00:00:00.000              |
| 2008-07-01 00:00:00.000              |
| 2009-07-01 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:
| createdby   |
|:------------|
| supermwt    |
| salesdesk   |
| mailmonitor |
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:
| key columns 2    |
|:-----------------|
| gbagada-nigeria  |
| ibadan-nigeria   |
| ikoyi-nigeria    |
| lekki-nigeria    |
| surulere-nigeria |
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:
| employee id   |
|:--------------|
| a             |
| c             |
| d             |
| e             |
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:
| paisregiao     |
|:---------------|
| united states  |
| united kingdom |
| canada         |
| germany        |
| france         |
| australia      |
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_name   |
|:---------------|
| france         |
| germany        |
| italy          |
| netherlands    |
| poland         |
| portugal       |
| scotland       |
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:
|  discount band    |
|:------------------|
| low               |
| medium            |
| high              |
| none              |
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:
| calyearmonth   |
|:---------------|
| december       |
| november       |
| october        |
| september      |
| august         |
| july           |
| march          |
| february       |
| january        |
| june           |
| may            |
| april          |
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_name     |
|:---------------|
| arizona        |
| arkansas       |
| california     |
| colorado       |
| connecticut    |
| delaware       |
| florida        |
| georgia        |
| idaho          |
| illinois       |
| indiana        |
| iowa           |
| kansas         |
| kentucky       |
| louisiana      |
| maine          |
| maryland       |
| massachusetts  |
| michigan       |
| minnesota      |
| mississippi    |
| missouri       |
| montana        |
| nebraska       |
| nevada         |
| new hampshire  |
| new jersey     |
| new mexico     |
| new york       |
| north carolina |
| north dakota   |
| ohio           |
| oklahoma       |
| oregon         |
| pennsylvania   |
| rhode island   |
| south carolina |
| south dakota   |
| tennessee      |
| texas          |
| utah           |
| virginia       |
| washington     |
| west virginia  |
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:
| team       |
|:-----------|
| patriots   |
| jaguars    |
| redskins   |
| rams       |
| raiders    |
| lions      |
| jets       |
| bears      |
| cardinals  |
| titans     |
| giants     |
| bills      |
| 49ers      |
| seahawks   |
| chargers   |
| texans     |
| dolphins   |
| cowboys    |
| falcons    |
| browns     |
| broncos    |
| chiefs     |
| panthers   |
| packers    |
| colts      |
| eagles     |
| vikings    |
| steelers   |
| ravens     |
| saints     |
| buccaneers |
| bengals    |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| product received date   |
|:------------------------|
| 2021-10-20 00:00:00.000 |
| 2021-10-25 00:00:00.000 |
| 2021-10-30 00:00:00.000 |
| 2021-11-04 00:00:00.000 |
| 2021-11-09 00:00:00.000 |
| 2021-11-14 00:00:00.000 |
| 2021-11-19 00:00:00.000 |
| 2021-11-24 00:00:00.000 |
| 2021-11-29 00:00:00.000 |
| 2021-12-04 00:00:00.000 |
| 2021-12-09 00:00:00.000 |
| 2021-12-14 00:00:00.000 |
| 2021-12-19 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:
| propertysets.pset_revit_other.occupancyzonename (ifclabel)   |
|:-------------------------------------------------------------|
| occupancy zone 1a                                            |
| occupancy zone 1d                                            |
| occupancy zone 1c                                            |
| occupancy zone 1e                                            |
| occupancy zone 1b                                            |
| occupancy zone 2a                                            |
| occupancy zone 2c                                            |
| occupancy zone 2d                                            |
| occupancy zone 2b                                            |
| occupancy zone 2r                                            |
| occupancy zone 3r                                            |
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 moddified          |
|:------------------------|
| 2019-01-02 08:00:00.000 |
| 2020-01-01 08:00:00.000 |
| 2018-11-05 08:30:00.000 |
| 2019-10-28 08:30:00.000 |
| 2019-10-28 08:20:00.000 |
| 2019-10-28 05:53:00.000 |
| 2019-10-28 08:00:00.000 |
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:
| year month   |
|:-------------|
| 2021 jul     |
| 2021 aug     |
| 2021 sep     |
| 2021 apr     |
| 2021 may     |
| 2021 jun     |
| 2021 oct     |
| 2021 nov     |
| 2021 dec     |
| 2021 jan     |
| 2021 feb     |
| 2021 mar     |
| 2022 jan     |
| 2022 feb     |
| 2022 mar     |
| 2022 apr     |
| 2022 may     |
| 2022 jun     |
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:
| fullname    |
|:------------|
| debra       |
| john        |
| sharon      |
| barbara     |
| brandon     |
| ryan        |
| ashley      |
| katherine   |
| richard     |
| nicole      |
| amanda      |
| joseph      |
| janet       |
| thomas      |
| sarah       |
| charles     |
| gary        |
| anna        |
| brenda      |
| rachel      |
| donald      |
| steven      |
| ruth        |
| mark        |
| ronald      |
| andrew      |
| george      |
| michelle    |
| joshua      |
| jack        |
| dennis      |
| stephen     |
| edward      |
| pamela      |
| nancy       |
| carol       |
| jessica     |
| benjamin    |
| jerry       |
| nicholas    |
| carolyn     |
| lisa        |
| angela      |
| alexander   |
| emma        |
| jacob       |
| stephanie   |
| raymond     |
| kimberly    |
| timothy     |
| cynthia     |
| elizabeth   |
| rebecca     |
| justin      |
| sandra      |
| jeffrey     |
| kevin       |
| eric        |
| dorothy     |
| donna       |
| laura       |
| patricia    |
| brian       |
| susan       |
| melissa     |
| virginia    |
| david       |
| jason       |
| margaret    |
| catherine   |
| daniel      |
| emily       |
| helen       |
| matthew     |
| gregory     |
| frank       |
| michael     |
| larry       |
| jonathan    |
| betty       |
| samantha    |
| karen       |
| paul        |
| scott       |
| jennifer    |
| amy         |
| christine   |
| shirley     |
| samuel      |
| mary        |
| patrick     |
| deborah     |
| anthony     |
| kathleen    |
| linda       |
| robert      |
| christopher |
| kenneth     |
| james       |
| william     |
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:
| modified                |
|:------------------------|
| 2016-06-20 04:25:47.000 |
| 2016-06-20 04:25:50.000 |
| 2016-06-20 04:25:53.000 |
| 2016-06-20 04:25:56.000 |
| 2016-07-12 10:00:24.000 |
| 2016-06-20 04:26:04.000 |
| 2015-12-24 16:12:33.000 |
| 2015-12-24 16:11:54.000 |
| 2015-12-24 16:11:56.000 |
| 2015-12-24 16:12:00.000 |
| 2015-12-24 16:12:03.000 |
| 2015-12-24 16:12:06.000 |
| 2015-12-24 16:12:08.000 |
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:
| toss_winner                 |
|:----------------------------|
| rajasthan royals            |
| kings xi punjab             |
| delhi daredevils            |
| mumbai indians              |
| chennai super kings         |
| kochi tuskers kerala        |
| deccan chargers             |
| pune warriors               |
| kolkata knight riders       |
| sunrisers hyderabad         |
| rising pune supergiants     |
| royal challengers bangalore |
| gujarat lions               |
| delhi capitals              |
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:
| continentexp   |
|:---------------|
| europe         |
| america        |
| asia           |
| africa         |
| oceania        |
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:
| column1   |
|:----------|
| c         |
| d         |
| e         |
| a         |