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:
| ehana name   |
|:-------------|
| a, lmhc      |
| n, lmhc      |
| , licsw      |
| y, lmhc      |
| i, cara      |
| a d, ma      |
| r, lmhc      |
| fnp-bc       |
| n, m.ed      |
| h, lmhc      |
| ra, msw      |
| t, lmhc      |
| a, m.d.      |
| , llcsw      |
| nda, ba      |
| awn, ms      |
| e, lcsw      |
| , m.ed.      |
| in, mba      |
| , lmhc       |
| l, m.d.      |
| mhnp-bc      |
| cole ma      |
| rn, bsn      |
| ie, msw      |
| on lmhc      |
| ine, bs      |
| cia, ma      |
| ate, ma      |
| ron, ma      |
| ina, ma      |
| ole, ma      |
| ale, ms      |
| n, lcsw      |
| rea, ma      |
| z, lcsw      |
| , ladc1      |
| lly, ma      |
| ney, ms      |
| ndi, ma      |
| cca, ma      |
| ia, msw      |
| fer, ma      |
| h, lcsw      |
| son, ma      |
| lle, ms      |
| y, lcsw      |
| uel, ma      |
| th, msw      |
| gan, ma      |
| nie, ma      |
| ett, ma      |
| ily, ms      |
| ina, md      |
| han, ma      |
| na, msw      |
| rah, ma      |
| ena, ma      |
| a, m.ed      |
| lle, ma      |
| -bc, np      |
| hel, ma      |
| lyn, bs      |
| yan, ma      |
| oah, ma      |
| basirat      |
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:
| artigos   |
|:----------|
| art. 24   |
| art. 34   |
| art. 54   |
| art. 44   |
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       |
|:------------|
| paper_5000  |
| paper_500   |
| paper_2000  |
| paper_15000 |
| paper_10000 |
| paper_1000  |
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:
| status   |
|:---------|
| pg       |
| ra       |
| ab       |
| ca       |
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             |
|:------------------------|
| 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 |
| 2021-01-31 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:
| commutedistance   |
|:------------------|
| 5-10 miles        |
| 1-2 miles         |
| 2-5 miles         |
| 10+ miles         |
| 0-1 miles         |
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:
| resultado   |
|:------------|
| perdedor    |
| vencedor    |
| empate      |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| mês       |
|:----------|
| julho     |
| agosto    |
| setembro  |
| outubro   |
| novembro  |
| janeiro   |
| fevereiro |
| março     |
| maio      |
| junho     |
| dezembro  |
| abril     |
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:
| nome do mês   |
|:--------------|
| janeiro       |
| fevereiro     |
| março         |
| abril         |
| maio          |
| junho         |
| julho         |
| agosto        |
| setembro      |
| outubro       |
| novembro      |
| dezembro      |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| tipo app                 |
|:-------------------------|
| aplicación               |
| aplicación especializada |
| items por demanda        |
| descuentos               |
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:
| folio   |
|:--------|
| x1      |
| x2      |
| x3      |
| x4      |
| x5      |
| x6      |
| x7      |
| x8      |
| x9      |
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:
| wake   |
|:-------|
| m      |
| h      |
| l      |
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                    |
|:------------------------|
| 2019-07-09 00:00:00.000 |
| 2019-07-10 00:00:00.000 |
| 2019-07-11 00:00:00.000 |
| 2019-07-12 00:00:00.000 |
| 2019-07-13 00:00:00.000 |
| 2019-07-14 00:00:00.000 |
| 2019-07-15 00:00:00.000 |
| 2019-07-16 00:00:00.000 |
| 2019-07-17 00:00:00.000 |
| 2019-07-18 00:00:00.000 |
| 2019-07-19 00:00:00.000 |
| 2019-07-20 00:00:00.000 |
| 2019-07-21 00:00:00.000 |
| 2019-07-22 00:00:00.000 |
| 2019-07-23 00:00:00.000 |
| 2019-07-24 00:00:00.000 |
| 2019-07-25 00:00:00.000 |
| 2019-07-26 00:00:00.000 |
| 2019-07-27 00:00:00.000 |
| 2019-07-28 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:
| date                    |
|:------------------------|
| 2017-03-01 00:00:00.000 |
| 2017-04-01 00:00:00.000 |
| 2017-05-01 00:00:00.000 |
| 2017-06-01 00:00:00.000 |
| 2017-01-01 00:00:00.000 |
| 2017-02-01 00:00:00.000 |
| 2016-12-01 00:00:00.000 |
| 2016-07-01 00:00:00.000 |
| 2016-08-01 00:00:00.000 |
| 2016-09-01 00:00:00.000 |
| 2016-10-01 00:00:00.000 |
| 2016-11-01 00:00:00.000 |
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:
| frequency   |
|:------------|
| monthly     |
| quarterly   |
| annual      |
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   |
|:-----------|
| fighting   |
| bluffing   |
| economic   |
| deduction  |
| party game |
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:
| grupo                               |
|:------------------------------------|
| secretaria do verde e meio ambiente |
| <grupo usuário não definido>        |
| gerencia de recursos naturais       |
| equipe da gestão                    |
| município são josé de ribamar       |
| comitê                              |
| compras                             |
| salc (compras)                      |
| inventário                          |
| pagamento                           |
| fiscal                              |
| recebimento                         |
| gestor fiscalização                 |
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:
| cat   |
|:------|
| sang  |
| xy    |
| ez    |
| str   |
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:
| grupo                        |
|:-----------------------------|
| sucos de uva (garrafa)       |
| cachaça                      |
| farinhas de trigo            |
| energéticos                  |
| vinho importado              |
| vinho tinto                  |
| vinho seco                   |
| sucos concentrado            |
| chás                         |
| café moído                   |
| temperos                     |
| macarrão parafuso            |
| farinhas                     |
| molho de pimenta             |
| azeites                      |
| leite em pó                  |
| café em cápsulas             |
| feijão preto                 |
| farofas                      |
| extrato de tomate            |
| creme de leite               |
| polvilhos                    |
| macarrão espaguete           |
| doces                        |
| doces enlatados              |
| pipocas                      |
| feijão carioca               |
| leite codensado              |
| farinhas de trigo fermentada |
| macarrão de lasanha          |
| floco de milho               |
| molhos de tomate             |
| açúcares                     |
| hortifruti                   |
| fermentos                    |
| óleos                        |
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:
| fechadivisa             |
|:------------------------|
| 2017-03-24 00:00:00.000 |
| 2017-03-17 00:00:00.000 |
| 2017-03-03 00:00:00.000 |
| 2017-05-09 00:00:00.000 |
| 2017-05-26 00:00:00.000 |
| 2017-06-05 00:00:00.000 |
| 2017-08-28 00:00:00.000 |
| 2018-02-24 00:00:00.000 |
| 2017-12-28 00:00:00.000 |
| 2017-06-23 00:00:00.000 |
| 2017-08-16 00:00:00.000 |
| 2017-08-15 00:00:00.000 |
| 2017-04-04 00:00:00.000 |
| 2017-07-01 00:00:00.000 |
| 2017-02-07 00:00:00.000 |
| 2017-01-24 00:00:00.000 |
| 2017-02-28 00:00:00.000 |
| 2017-02-27 00:00:00.000 |
| 2017-02-15 00:00:00.000 |
| 2017-07-05 00:00:00.000 |
| 2017-08-02 00:00:00.000 |
| 2017-04-01 00:00:00.000 |
| 2017-03-27 00:00:00.000 |
| 2017-11-24 00:00:00.000 |
| 2017-12-05 00:00:00.000 |
| 2017-12-20 00:00:00.000 |
| 2017-12-19 00:00:00.000 |
| 2018-06-27 00:00:00.000 |
| 2018-07-16 00:00:00.000 |
| 2018-07-20 00:00:00.000 |
| 2018-07-24 00:00:00.000 |
| 2018-07-25 00:00:00.000 |
| 2018-08-14 00:00:00.000 |
| 2018-08-16 00:00:00.000 |
| 2018-09-10 00:00:00.000 |
| 2018-09-11 00:00:00.000 |
| 2018-09-12 00:00:00.000 |
| 2018-09-13 00:00:00.000 |
| 2018-09-14 00:00:00.000 |
| 2018-09-18 00:00:00.000 |
| 2018-09-19 00:00:00.000 |
| 2018-09-24 00:00:00.000 |
| 2018-09-25 00:00:00.000 |
| 2018-10-01 00:00:00.000 |
| 2018-10-04 00:00:00.000 |
| 2018-10-08 00:00:00.000 |
| 2018-10-09 00:00:00.000 |
| 2018-10-10 00:00:00.000 |
| 2018-10-15 00:00:00.000 |
| 2018-10-16 00:00:00.000 |
| 2018-10-24 00:00:00.000 |
| 2018-10-26 00:00:00.000 |
| 2018-10-30 00:00:00.000 |
| 2018-11-05 00:00:00.000 |
| 2018-11-08 00:00:00.000 |
| 2018-11-12 00:00:00.000 |
| 2018-07-05 00:00:00.000 |
| 2018-07-30 00:00:00.000 |
| 2018-08-28 00:00:00.000 |
| 2018-06-11 00:00:00.000 |
| 2018-07-17 00:00:00.000 |
| 2018-08-07 00:00:00.000 |
| 2018-06-19 00:00:00.000 |
| 2018-07-13 00:00:00.000 |
| 2018-07-18 00:00:00.000 |
| 2018-08-13 00:00:00.000 |
| 2018-09-04 00:00:00.000 |
| 2018-09-05 00:00:00.000 |
| 2018-09-20 00:00:00.000 |
| 2018-10-23 00:00:00.000 |
| 2018-10-29 00:00:00.000 |
| 2018-11-09 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:
| region        |
|:--------------|
| bangalore     |
| europe        |
| china         |
| franklin park |
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:
| minrecievedate          |
|:------------------------|
| 2020-01-23 00:00:00.000 |
| 2020-01-16 00:00:00.000 |
| 2020-01-08 00:00:00.000 |
| 2020-01-03 00:00:00.000 |
| 2020-01-24 00:00:00.000 |
| 2020-01-09 00:00:00.000 |
| 2020-01-17 00:00:00.000 |
| 2020-01-20 00:00:00.000 |
| 2020-01-14 00:00:00.000 |
| 2020-01-10 00:00:00.000 |
| 2020-01-07 00:00:00.000 |
| 2020-01-29 00:00:00.000 |
| 2020-02-20 00:00:00.000 |
| 2020-02-05 00:00:00.000 |
| 2020-02-11 00:00:00.000 |
| 2020-02-13 00:00:00.000 |
| 2020-01-21 00:00:00.000 |
| 2020-02-10 00:00:00.000 |
| 2020-01-22 00:00:00.000 |
| 2020-01-28 00:00:00.000 |
| 2020-03-18 00:00:00.000 |
| 2020-01-30 00:00:00.000 |
| 2020-02-06 00:00:00.000 |
| 2020-02-17 00:00:00.000 |
| 2020-02-21 00:00:00.000 |
| 2020-03-16 00:00:00.000 |
| 2020-03-02 00:00:00.000 |
| 2020-03-06 00:00:00.000 |
| 2020-02-25 00:00:00.000 |
| 2020-03-19 00:00:00.000 |
| 2020-03-03 00:00:00.000 |
| 2020-03-26 00:00:00.000 |
| 2020-04-13 00:00:00.000 |
| 2020-04-17 00:00:00.000 |
| 2020-04-23 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2020-05-26 00:00:00.000 |
| 2020-04-03 00:00:00.000 |
| 2020-05-28 00:00:00.000 |
| 2020-05-11 00:00:00.000 |
| 2020-04-24 00:00:00.000 |
| 2020-05-08 00:00:00.000 |
| 2020-05-27 00:00:00.000 |
| 2020-06-10 00:00:00.000 |
| 2020-06-16 00:00:00.000 |
| 2020-06-08 00:00:00.000 |
| 2020-06-05 00:00:00.000 |
| 2020-06-09 00:00:00.000 |
| 2020-05-19 00:00:00.000 |
| 2020-02-27 00:00:00.000 |
| 2020-02-24 00:00:00.000 |
| 2020-02-12 00:00:00.000 |
| 2020-03-24 00:00:00.000 |
| 2020-03-17 00:00:00.000 |
| 2020-04-15 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-06-01 00:00:00.000 |
| 2020-06-23 00:00:00.000 |
| 2020-06-17 00:00:00.000 |
| 2020-06-02 00:00:00.000 |
| 2020-06-12 00:00:00.000 |
| 2020-06-22 00:00:00.000 |
| 2020-06-18 00:00:00.000 |
| 2020-05-20 00:00:00.000 |
| 2020-05-15 00:00:00.000 |
| 2020-06-29 00:00:00.000 |
| 2020-06-24 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:
| to                      |
|:------------------------|
| 2019-03-31 00:00:00.000 |
| 2016-03-31 00:00:00.000 |
| 2017-03-31 00:00:00.000 |
| 2018-10-15 00:00:00.000 |
| 2019-08-31 00:00:00.000 |
| 2018-03-31 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:
| day of week full   |
|:-------------------|
| friday             |
| saturday           |
| sunday             |
| tuesday            |
| wednesday          |
| thursday           |
| monday             |
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:
| unit of measure   |
|:------------------|
| piece             |
| box               |
| stück             |
| can               |
| pallet            |
| stk               |
| ds                |
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:
| fiscalyear   |
|:-------------|
| fy19         |
| fy20         |
| fy18         |
| fy17         |
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:
| client   |
|:---------|
| b        |
| c        |
| d        |
| e        |
| f        |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| dayofweek   |
|:------------|
| vrijdag     |
| zaterdag    |
| zondag      |
| maandag     |
| dinsdag     |
| woensdag    |
| donderdag   |
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:
| connector type   |
|:-----------------|
| evo2             |
| t4pc             |
| mc4              |
| utx              |
| t4ppe            |
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:
| 2023   |
|:-------|
| e      |
| f      |
| g      |
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:
| function              |
|:----------------------|
| loan officer          |
| borrower              |
| lender processor      |
| lender underwriter    |
| lender closer         |
| title/closing company |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| segment     |
|:------------|
| consumer    |
| corporate   |
| home office |
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:
| weather_forecast   |
|:-------------------|
| very good          |
| poor               |
| very poor          |
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 name     |
|:------------------|
| jamaal allbright  |
| cleveland heard   |
| celeste isenhour  |
| stacy miramontes  |
| lindsey chatman   |
| alia messing      |
| lon thorne        |
| kyung wheeless    |
| denese madera     |
| eliseo dabney     |
| ismael spearman   |
| jerome reich      |
| tonisha stennis   |
| sydney whitcomb   |
| nannette millener |
| alaina munford    |
| shirlene mcvay    |
| catheryn araujo   |
| octavia karnes    |
| melonie cataldo   |
| illa clauss       |
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:
| organization                                                                                         |
|:-----------------------------------------------------------------------------------------------------|
| innovation, science and economic development canada                                                  |
| enterprises##n##innovation, science and economic development canada                                  |
| innovation, science and economic development canada##n##enterprises                                  |
| federal government##n##innovation, science and economic development canada                           |
| federal government                                                                                   |
| enterprises                                                                                          |
| not applicable                                                                                       |
| provincial government                                                                                |
| innovation, science and economic development canada##n##federal government                           |
| innovation, science and economic development canada##n##provincial government                        |
| federal government##n##provincial government##n##innovation, science and economic development canada |
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:
| class     |
|:----------|
| 3rd class |
| 1st class |
| 2nd class |
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:
| daybyday                |
|:------------------------|
| 2018-12-10 00:00:00.000 |
| 2018-12-11 00:00:00.000 |
| 2018-12-12 00:00:00.000 |
| 2018-12-13 00:00:00.000 |
| 2018-12-14 00:00:00.000 |
| 2018-12-15 00:00:00.000 |
| 2018-12-16 00:00:00.000 |
| 2018-12-17 00:00:00.000 |
| 2018-12-18 00:00:00.000 |
| 2018-12-08 00:00:00.000 |
| 2018-12-09 00:00:00.000 |
| 2018-12-19 00:00:00.000 |
| 2018-12-20 00:00:00.000 |
| 2018-12-21 00:00:00.000 |
| 2018-12-22 00:00:00.000 |
| 2018-12-23 00:00:00.000 |
| 2018-12-24 00:00:00.000 |
| 2018-12-25 00:00:00.000 |
| 2018-12-26 00:00:00.000 |
| 2018-12-27 00:00:00.000 |
| 2018-12-28 00:00:00.000 |
| 2018-12-29 00:00:00.000 |
| 2018-12-30 00:00:00.000 |
| 2018-12-31 00:00:00.000 |
| 2019-01-01 00:00:00.000 |
| 2019-01-02 00:00:00.000 |