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:
| marital   |
|:----------|
| married   |
| single    |
| divorced  |
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:
| sample file                           |
|:--------------------------------------|
| procurement analysis sample pbix      |
| retail analysis sample pbix           |
| sales and marketing sample pbix       |
| supplier quality analysis sample pbix |
| customer profitability sample         |
| human resources sample                |
| opportunity tracking sample           |
| procurement analysis sample           |
| retail analysis sample                |
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:
| customer_age_category   |
|:------------------------|
| middle_ages_adults      |
| old_adults              |
| young_adults            |
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                    |
|:------------------------|
| 2021-10-23 00:00:00.000 |
| 2021-10-12 00:00:00.000 |
| 2021-10-02 00:00:00.000 |
| 2021-09-20 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:
| account                           |
|:----------------------------------|
| other revenue                     |
| shrink wrap install               |
| transport charge                  |
| equipment hire                    |
| fuel truck and van                |
| intaks rent monthly payment       |
| materials                         |
| rubbish diposal                   |
| shrink wrap costs                 |
| acc                               |
| accounting fees                   |
| donations                         |
| freight & courier                 |
| general expenses                  |
| home office expenses              |
| ird penalties                     |
| legal expenses                    |
| loss on sale of fixed assets      |
| professional memberships          |
| safety testing & maintenance      |
| staff recruitment cost            |
| tools < $500                      |
| tools > $500                      |
| travel - international            |
| travel - national                 |
| erect and dismantle               |
| hire & rental                     |
| labour only                       |
| mobile scaffold                   |
| sales netting                     |
| total income                      |
| scaffolding rental                |
| sub contractor services           |
| wages                             |
| total cost of sales               |
| gross profit                      |
| advertising                       |
| bank fees                         |
| consulting fees                   |
| depreciation                      |
| entertainment                     |
| finance charge                    |
| industrial place expenses         |
| industrial place rent             |
| insurance                         |
| interest expense                  |
| kiwisaver employer contributions  |
| light, power, heating             |
| motor vehicle expenses (not fuel) |
| office expenses                   |
| safety clothing & equipment       |
| salaries - proprietors (paye)     |
| software expenses                 |
| staff training                    |
| telephone & internet              |
| vehicle lease to buy              |
| total operating expenses          |
| net profit                        |


label: rubbish diposal
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   |
|:----------|
| mexico    |
| peru      |
| argentina |
| chile     |
| colombia  |
| brazil    |
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_code   |
|:-------------|
| mh           |
| kl           |
| ap           |
| mp           |
| wb           |
| up           |
| ts           |
| dl           |
| tn           |
| jk           |
| py           |
| rj           |
| hp           |
| sk           |
| as           |
| ch           |
| uk           |
| gj           |
| mn           |
| ar           |
| ka           |
| od           |
| hr           |
| pb           |
| ga           |
| mz           |
| rt           |
| tr           |
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:
| visit type           |
|:---------------------|
| follow up            |
| initial evaluation   |
| pelvic               |
| bike fit - insurance |
| re evaluation        |
| bike fit - cash      |
| return patient       |
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:
| produtotipo           |
|:----------------------|
| fruit ice uva         |
| fruit ice melancia    |
| fruit ice limão       |
| cream ice goiabinha   |
| cream ice pedaços     |
| fruit ice pedaços     |
| fruit ice açai        |
| fruit ice kiwi        |
| fruit ice maracujá    |
| cream ice flocos      |
| cream ice chocolate   |
| cream ice morango     |
| fruit ice manga       |
| cream ice abacaxi     |
| fruit ice creme       |
| fruit ice flocos      |
| cream ice pistache    |
| fruit ice melão       |
| fruit ice chocolate   |
| fruit ice morango     |
| fruit ice napolitano  |
| fruit ice cereja/maça |
| cream ice doce        |
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 category   |
|:-------------------|
| technology         |
| furniture          |
| office supplies    |
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:
| quarterincalendar   |
|:--------------------|
| q3 2013             |
| q3 2014             |
| q3 2015             |
| q3 2016             |
| q3 2017             |
| q3 2018             |
| q3 2019             |
| q3 2020             |
| q3 2021             |
| q3 2022             |
| q3 2023             |
| q3 2024             |
| q3 2025             |
| q4 2013             |
| q4 2014             |
| q4 2015             |
| q4 2016             |
| q4 2017             |
| q4 2018             |
| q4 2019             |
| q4 2020             |
| q4 2021             |
| q4 2022             |
| q4 2023             |
| q4 2024             |
| q4 2025             |
| q2 2013             |
| q2 2014             |
| q2 2015             |
| q2 2016             |
| q2 2017             |
| q2 2018             |
| q2 2019             |
| q2 2020             |
| q2 2021             |
| q2 2022             |
| q2 2023             |
| q2 2024             |
| q2 2025             |
| q1 2013             |
| q1 2014             |
| q1 2016             |
| q1 2017             |
| q1 2018             |
| q1 2019             |
| q1 2020             |
| q1 2021             |
| q1 2022             |
| q1 2023             |
| q1 2024             |
| q1 2025             |
| q1 2015             |
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:
| storetype   |
|:------------|
| store       |
| online      |
| catalog     |
| reseller    |
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:
| county          |
|:----------------|
| trinity         |
| lassen          |
| san benito      |
| mariposa        |
| sutter          |
| plumas          |
| glenn           |
| modoc           |
| inyo            |
| tuolumne        |
| amador          |
| del norte       |
| yolo            |
| lake            |
| siskiyou        |
| tehama          |
| colusa          |
| mono            |
| nevada          |
| mendocino       |
| el dorado       |
| humboldt        |
| napa            |
| shasta          |
| calaveras       |
| yuba            |
| butte           |
| madera          |
| san luis obispo |
| santa cruz      |
| sonoma          |
| kings           |
| monterey        |
| merced          |
| santa barbara   |
| marin           |
| imperial        |
| tulare          |
| kern            |
| san francisco   |
| san mateo       |
| san joaquin     |
| ventura         |
| solano          |
| placer          |
| contra costa    |
| santa clara     |
| stanislaus      |
| fresno          |
| sacramento      |
| orange          |
| san bernardino  |
| riverside       |
| alameda         |
| san diego       |
| los angeles     |
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:
| heropowerid   |
|:--------------|
| dr1           |
| rg1           |
| dr2           |
| sh1           |
| dr3           |
| pr1           |
| dh3           |
| dh2           |
| mg1           |
| dh1           |
| wr2           |
| wr3           |
| sh2           |
| wr1           |
| wl3           |
| hn1           |
| pl2           |
| pr2           |
| sh3           |
| mg3           |
| wl1           |
| wl2           |
| pl1           |
| hn2           |
| pl3           |
| hn3           |
| pr3           |
| rg2           |
| rg3           |
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:
| languages.3   |
|:--------------|
| en            |
| hy            |
| de-be         |
| ay            |
| sr-ba         |
| ms-cc         |
| fr            |
| ru            |
| lt            |
| pl            |
| fr-lu         |
| it            |
| es            |
| fil           |
| ln-cg         |
| tem           |
| uz            |
| tg            |
| fr-vu         |
| fr-wf         |
| sre           |
| kg            |
| rom           |
| fia           |
| ar            |
| uz-af         |
| hu            |
| fa            |
| ln            |
| fo            |
| de            |
| nl            |
| en-il         |
| fr-it         |
| zu            |
| tr            |
| tum           |
| zh            |
| snk           |
| pon           |
| gag           |
| bs            |
| kr            |
| yo            |
| nn            |
| bal           |
| pa            |
| en-pw         |
| meu           |
| fr-rw         |
| fuc           |
| sq            |
| ms-sg         |
| af            |
| gl            |
| srn           |
| sma           |
| it-ch         |
| hna           |
| diq           |
| sm            |
| sw            |
| haw           |
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:
| door_pockets        |
|:--------------------|
| front               |
| front & rear        |
| yes                 |
| front, front & rear |
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:
| contentdate             |
|:------------------------|
| 2020-04-06 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-02-29 00:00:00.000 |
| 2020-01-31 00:00:00.000 |
| 2019-12-31 00:00:00.000 |
| 2019-11-30 00:00:00.000 |
| 2019-10-31 00:00:00.000 |
| 2019-09-30 00:00:00.000 |
| 2019-08-31 00:00:00.000 |
| 2019-07-31 00:00:00.000 |
| 2019-06-30 00:00:00.000 |
| 2019-05-31 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:
| sub_regional_chapter_name   |
|:----------------------------|
| kaikoura                    |
| hurunui - waiau             |
| waimakariri                 |
| christchurch - west melton  |
| selwyn - waihora            |
| alpine river                |
| banks peninsula             |
| orari - opihi - pareora     |
| ashburton                   |
| waitaki                     |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| quarter year   |
|:---------------|
| q3 2017        |
| q4 2017        |
| q1 2018        |
| q2 2018        |
| q1 2019        |
| q2 2019        |
| q3 2018        |
| q4 2018        |
| q3 2019        |
| q4 2019        |
| q1 2020        |
| q2 2020        |
| q3 2020        |
| q4 2020        |
| q1 2017        |
| q2 2017        |
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:
| iso3   |
|:-------|
| ben    |
| bfa    |
| cmr    |
| caf    |
| tcd    |
| cog    |
| egy    |
| eth    |
| gab    |
| gha    |
| gin    |
| gnb    |
| ken    |
| lbr    |
| mdg    |
| mwi    |
| mli    |
| mrt    |
| mus    |
| mar    |
| moz    |
| ner    |
| nga    |
| rwa    |
| sen    |
| sle    |
| zaf    |
| sdn    |
| swz    |
| tza    |
| tgo    |
| uga    |
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:
| athlete   |
|:----------|
| 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:
| streamingtv         |
|:--------------------|
| yes                 |
| no                  |
| no internet service |
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       |
|:------------|
| 31-60 days  |
| 61-90 days  |
| 90-120 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:
| monthincalendar   |
|:------------------|
| jul 2018          |
| aug 2018          |
| sep 2018          |
| oct 2018          |
| nov 2018          |
| dec 2018          |
| jan 2019          |
| feb 2019          |
| mar 2019          |
| apr 2019          |
| may 2019          |
| jun 2019          |
| oct 2019          |
| nov 2019          |
| dec 2019          |
| jul 2019          |
| aug 2019          |
| sep 2019          |
| jan 2018          |
| feb 2018          |
| mar 2018          |
| apr 2018          |
| may 2018          |
| jun 2018          |
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:
| monthending             |
|:------------------------|
| 2017-03-31 00:00:00.000 |
| 2017-04-30 00:00:00.000 |
| 2017-05-31 00:00:00.000 |
| 2017-06-30 00:00:00.000 |
| 2017-07-31 00:00:00.000 |
| 2017-08-31 00:00:00.000 |
| 2017-09-30 00:00:00.000 |
| 2017-10-31 00:00:00.000 |
| 2017-11-30 00:00:00.000 |
| 2017-12-31 00:00:00.000 |
| 2018-01-31 00:00:00.000 |
| 2018-02-28 00:00:00.000 |
| 2018-03-31 00:00:00.000 |
| 2018-04-30 00:00:00.000 |
| 2018-05-31 00:00:00.000 |
| 2018-06-30 00:00:00.000 |
| 2018-07-31 00:00:00.000 |
| 2018-08-31 00:00:00.000 |
| 2018-09-30 00:00:00.000 |
| 2018-10-31 00:00:00.000 |
| 2018-11-30 00:00:00.000 |
| 2018-12-31 00:00:00.000 |
| 2019-01-31 00:00:00.000 |
| 2019-02-28 00:00:00.000 |
| 2019-03-31 00:00:00.000 |
| 2019-04-30 00:00:00.000 |
| 2019-05-31 00:00:00.000 |
| 2019-06-30 00:00:00.000 |
| 2019-07-31 00:00:00.000 |
| 2019-08-31 00:00:00.000 |
| 2019-09-30 00:00:00.000 |
| 2019-10-31 00:00:00.000 |
| 2019-11-30 00:00:00.000 |
| 2019-12-31 00:00:00.000 |
| 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 |
| 2017-01-31 00:00:00.000 |
| 2017-02-28 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:
| service start   |
|:----------------|
| ene             |
| feb             |
| mar             |
| 0               |
| jun             |
| abr             |
| ago             |
| sept            |
| may             |
| nov             |
| jul             |


label: 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:
| recruitment id   |
|:-----------------|
| r3               |
| r4               |
| r5               |
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:
| licence             |
|:--------------------|
| lic-k5yzbqw71h-2018 |
| lic-pldj67543h-2018 |
| lic-n3wulwuqxh-2018 |
| lic-zkhotinq2a-2018 |
| lic-v8o8mghhj8-2018 |
| lic-xyofklib3k-2018 |
| lic-pp1bigldtc-2018 |
| lic-e59s41hyma-2018 |
| lic-l9txpq9c8o-2018 |
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 name   |
|:-------------|
| june         |
| december     |
| march        |
| july         |
| august       |
| september    |
| october      |
| february     |
| november     |
| january      |
| april        |
| may          |
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 of week             |
|:------------------------|
| 2021-01-09 00:00:00.000 |
| 2021-01-16 00:00:00.000 |
| 2021-01-23 00:00:00.000 |
| 2021-01-02 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| 年份   |
|:-------|
| 106年  |
| 107年  |
| 108年  |
| 104年  |
| 105年  |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| category        |
|:----------------|
| office supplies |
| technology      |
| furniture       |
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:
| deliver_by              |
|:------------------------|
| 2019-01-25 00:00:00.000 |
| 2019-01-24 00:00:00.000 |
| 2019-01-23 00:00:00.000 |
| 2019-01-28 00:00:00.000 |
| 2019-01-22 00:00:00.000 |
| 2019-01-21 00:00:00.000 |
| 2019-01-18 00:00:00.000 |
| 2019-01-17 00:00:00.000 |
| 2019-01-15 00:00:00.000 |
| 2019-01-16 00:00:00.000 |
| 2019-01-11 00:00:00.000 |
| 2019-01-14 00:00:00.000 |
| 2019-01-10 00:00:00.000 |
| 2019-01-09 00:00:00.000 |
| 2019-01-08 00:00:00.000 |
| 2019-01-07 00:00:00.000 |
| 2019-01-04 00:00:00.000 |
| 2019-01-03 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| payment date :          |
|:------------------------|
| 2020-02-02 00:00:00.000 |
| 2020-06-06 00:00:00.000 |
| 2020-05-04 00:00:00.000 |
| 2020-05-13 00:00:00.000 |
| 2020-07-13 00:00:00.000 |
| 2020-04-15 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2020-06-16 00:00:00.000 |
| 2020-08-21 00:00:00.000 |
| 2020-08-01 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:
| xim_start date          |
|:------------------------|
| 2021-04-01 00:00:00.000 |
| 2021-05-01 00:00:00.000 |
| 2021-06-01 00:00:00.000 |
| 2021-07-01 00:00:00.000 |