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:
| quarterincalendar   |
|:--------------------|
| q3 2012             |
| q2 2012             |
| q4 2012             |
| q1 2012             |
| q1 2011             |
| q4 2011             |
| q2 2011             |
| q3 2011             |
| q3 2013             |
| q2 2013             |
| q4 2013             |
| q1 2013             |
| q4 2010             |
| q1 2014             |
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:
| niname                        |
|:------------------------------|
| anybank 1% 25/8/22            |
| sukuk 2.5 11-7-22             |
| rep poland tf 4% ge24 usd     |
| sukuk 0.05 coupon             |
| bfcm_frn_23/03/17             |
| ukt_4.50_07/03/19             |
| hsbc hkd 1% aug 2018          |
| 2 yr allianz                  |
| barclays plc 2.125pc 24oct49  |
| alphabank                     |
| bond: usd 10% 12/05/2025      |
| bank1_10perc                  |
| werwerw                       |
| airnz 2% 15-9-2017            |
| bd: barclays 6% usd 10/1/2019 |
| barclays usd 5% 13/2/23       |
| deutsche 3.75 09/20           |
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:
| 10. community need: maintaining housing             |
|:----------------------------------------------------|
| help affording home repairs/weather-proofing        |
| eviction defense/prevention programs                |
| more accessible housing for those with disabilities |
| other                                               |
| rent/utility help                                   |
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:
| firmante                        |
|:--------------------------------|
| antonio sánchez laguna          |
| daniel cañete                   |
| cliente mantenedor roman        |
| jesús plaza sánchez             |
| maría del carmen díez cendón    |
| luis gonzález clemente          |
| fernando barahona de andrés     |
| juan carlos rodero hernández    |
| pedro gonzález martín           |
| cesar saiz regidor              |
| francisco rodríguez flores      |
| luis calle picado               |
| francisco javier méntrida pérez |
| jose maría blanco vilela        |
| tomás esteban cebolla           |
| luis leyva                      |
| carlos tortajada montuenga      |
| castor garcía lacal             |
| mauricio garcía arias           |
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 ano   |
|:----------|
| jan 17    |
| fev 17    |
| mar 17    |
| abr 17    |
| mai 17    |
| jun 17    |
| jul 17    |
| ago 17    |
| set 17    |
| out 17    |
| nov 17    |
| dez 17    |
| jan 18    |
| fev 18    |
| mar 18    |
| abr 18    |
| mai 18    |
| jun 18    |
| jul 18    |
| ago 18    |
| set 18    |
| out 18    |
| set 16    |
| out 16    |
| nov 16    |
| dez 16    |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| employee   |
|:-----------|
| carter     |
| david      |
| elon       |
| fred       |
| greg       |
| harry      |
| ian        |
| julie      |
| kyle       |
| louise     |
| mary       |
| newman     |
| oscar      |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| date                    |
|:------------------------|
| 2020-01-02 00:00:00.000 |
| 2020-01-03 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| color        |
|:-------------|
| multi        |
| red          |
| black        |
| white        |
| grey         |
| blue         |
| silver       |
| yellow       |
| silver/black |
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:
| jobsat                             |
|:-----------------------------------|
| slightly dissatisfied              |
| very satisfied                     |
| very dissatisfied                  |
| neither satisfied nor dissatisfied |
| slightly satisfied                 |
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:
| license_date            |
|:------------------------|
| 2019-11-12 00:00:00.000 |
| 2020-06-30 00:00:00.000 |
| 2020-05-20 00:00:00.000 |
| 2020-08-19 00:00:00.000 |
| 2019-07-06 00:00:00.000 |
| 2019-06-19 00:00:00.000 |
| 2019-05-30 00:00:00.000 |
| 2020-08-04 00:00:00.000 |
| 2020-04-20 00:00:00.000 |
| 2019-10-13 00:00:00.000 |
| 2020-06-06 00:00:00.000 |
| 2021-01-19 00:00:00.000 |
| 2019-07-09 00:00:00.000 |
| 2020-03-08 00:00:00.000 |
| 2020-08-21 00:00:00.000 |
| 2020-04-06 00:00:00.000 |
| 2020-08-03 00:00:00.000 |
| 2021-03-21 00:00:00.000 |
| 2019-08-28 00:00:00.000 |
| 2020-01-13 00:00:00.000 |
| 2020-02-12 00:00:00.000 |
| 2019-12-18 00:00:00.000 |
| 2019-07-01 00:00:00.000 |
| 2019-08-16 00:00:00.000 |
| 2020-09-07 00:00:00.000 |
| 2019-05-21 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:
| sales region   |
|:---------------|
| western region |
| eastern region |
| central region |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| month-year     |
|:---------------|
| january-2015   |
| february-2015  |
| march-2015     |
| april-2015     |
| may-2015       |
| june-2015      |
| july-2015      |
| august-2015    |
| september-2015 |
| october-2015   |
| november-2015  |
| december-2015  |
| january-2016   |
| february-2016  |
| march-2016     |
| april-2016     |
| may-2016       |
| june-2016      |
| july-2016      |
| august-2016    |
| september-2016 |
| october-2016   |
| november-2016  |
| december-2016  |
| january-2017   |
| february-2017  |
| march-2017     |
| april-2017     |
| may-2017       |
| june-2017      |
| july-2017      |
| august-2017    |
| september-2017 |
| october-2017   |
| november-2017  |
| december-2017  |
| january-2018   |
| february-2018  |
| march-2018     |
| april-2018     |
| may-2018       |
| june-2018      |
| july-2018      |
| august-2018    |
| september-2018 |
| october-2018   |
| november-2018  |
| december-2018  |
| january-2019   |
| february-2019  |
| march-2019     |
| april-2019     |
| may-2019       |
| june-2019      |
| july-2019      |
| august-2019    |
| september-2019 |
| october-2019   |
| november-2019  |
| december-2019  |
| january-2020   |
| february-2020  |
| march-2020     |
| april-2020     |
| may-2020       |
| june-2020      |
| july-2020      |
| august-2020    |
| september-2020 |
| october-2020   |
| november-2020  |
| december-2020  |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| 구군시   |
|:---------|
| 천안시   |
| 나주시   |
| 아산시   |
| 전주시   |
| 관악구   |
| 달서구   |
| 수원시   |
| 영천시   |
| 중구     |
| 안양시   |
| 제주시   |
| 수영구   |
| 창원시   |
| 여수시   |
| 화성시   |
| 양산시   |
| 광산구   |
| 강남구   |
| 부산진구 |
| 연수구   |
| 남구     |
| 세종시   |
| 안산시   |
| 청주시   |
| 음성군   |
| 금정구   |
| 오산시   |
| 강릉시   |
| 유성구   |
| 영등포구 |
| 계양구   |
| 동구     |
| 북구     |
| 서귀포시 |
| 수성구   |
| 동래구   |
| 강동구   |
| 강북구   |
| 남동구   |
| 울주군   |
| 경주시   |
| 익산시   |
| 부평구   |
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                   |
|:------------------------|
| 2021-10-01 00:00:00.000 |
| 2021-07-01 00:00:00.000 |
| 2021-01-01 00:00:00.000 |
| 2020-10-01 00:00:00.000 |
| 2020-08-01 00:00:00.000 |
| 2020-07-01 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2020-04-01 00:00:00.000 |
| 2020-02-01 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
| 2019-11-01 00:00:00.000 |
| 2019-10-01 00:00:00.000 |
| 2019-08-01 00:00:00.000 |
| 2019-07-01 00:00:00.000 |
| 2019-05-01 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2019-02-01 00:00:00.000 |
| 2019-01-01 00:00:00.000 |
| 2018-11-01 00:00:00.000 |
| 2018-10-01 00:00:00.000 |
| 2018-08-01 00:00:00.000 |
| 2018-07-01 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-04-01 00:00:00.000 |
| 2018-02-01 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2017-11-01 00:00:00.000 |
| 2017-10-01 00:00:00.000 |
| 2017-08-01 00:00:00.000 |
| 2017-07-01 00:00:00.000 |
| 2017-05-01 00:00:00.000 |
| 2017-04-01 00:00:00.000 |
| 2017-02-01 00:00:00.000 |
| 2017-01-01 00:00:00.000 |
| 2022-01-01 00:00:00.000 |
| 2021-09-01 00:00:00.000 |
| 2021-06-01 00:00:00.000 |
| 2020-09-01 00:00:00.000 |
| 2020-06-01 00:00:00.000 |
| 2020-03-01 00:00:00.000 |
| 2019-09-01 00:00:00.000 |
| 2019-06-01 00:00:00.000 |
| 2019-03-01 00:00:00.000 |
| 2018-09-01 00:00:00.000 |
| 2021-05-01 00:00:00.000 |
| 2021-04-01 00:00:00.000 |
| 2021-03-01 00:00:00.000 |
| 2018-12-01 00:00:00.000 |
| 2021-12-01 00:00:00.000 |
| 2021-11-01 00:00:00.000 |
| 2021-08-01 00:00:00.000 |
| 2021-02-01 00:00:00.000 |
| 2020-12-01 00:00:00.000 |
| 2020-11-01 00:00:00.000 |
| 2019-12-01 00:00:00.000 |
| 2018-06-01 00:00:00.000 |
| 2018-03-01 00:00:00.000 |
| 2017-12-01 00:00:00.000 |
| 2017-09-01 00:00:00.000 |
| 2017-06-01 00:00:00.000 |
| 2017-03-01 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:
| haircarestabilityrequests.product   |
|:------------------------------------|
| form-000000034454                   |
| form-000000033444                   |
| form-000000045995                   |
| form-000000034511                   |
| form-000000032956                   |
| form-000000038069                   |
| form-000000025055                   |
| form-000000037018                   |
| form-000000018782                   |
| form-000000038500                   |
| form-000000039179                   |
| form-000000044858                   |
| form-000000041105                   |
| form-000000044861                   |
| form-000000040335                   |
| form-000000035108                   |
| form-000000042536                   |
| form-000000034518                   |
| form-000000040234                   |
| form-000000031458                   |
| form-000000036282                   |
| form-000000043139                   |
| form-000000031391                   |
| form-000000035148                   |
| form-000000034185                   |
| form-000000036664                   |
| form-000000036164                   |
| form-000000035151                   |
| form-000000034512                   |
| form-000000039568                   |
| form-000000035152                   |
| form-000000032769                   |
| form-000000038006                   |
| form-000000034621                   |
| form-000000037831                   |
| form-000000019825                   |
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:
| job satisfication (new)   |
|:--------------------------|
| low                       |
| medium                    |
| high                      |
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:
| stock codes   |
|:--------------|
| abb           |
| abcapital     |
| acc           |
| ambujacem     |
| ashokley      |
| auropharma    |
| bankbaroda    |
| bel           |
| bhel          |
| boschltd      |
| britannia     |
| cadilahc      |
| colpal        |
| concor        |
| cumminsind    |
| dabur         |
| dlf           |
| dmart         |
| emamiltd      |
| gicre         |
| godrejcp      |
| havells       |
| hindzinc      |
| icicipruli    |
| idea          |
| indigo        |
| jswsteel      |
| l&tfh         |
| lichsgfin     |
| marico        |
| mcdowell-n    |
| mothersumi    |
| mrf           |
| nhpc          |
| nmdc          |
| ofss          |
| oil           |
| pel           |
| petronet      |
| pfc           |
| pghh          |
| pidilitind    |
| pnb           |
| recltd        |
| sail          |
| sbilife       |
| shreecem      |
| siemens       |
| srtransfin    |
| suntv         |
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:
| position_unique   |
|:------------------|
| p_3924239_aa      |
| p_2395212         |
| p_5380911         |
| p_8439812         |
| p_8439812_a       |
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:
| куратор                  |
|:-------------------------|
| hr                       |
| технический департамент  |
| cfo                      |
| коммерческий департамент |
| департамент безопасности |
| департамент it           |
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       |
|:-------------|
| noa          |
| nea          |
| cuyo         |
| pampeana     |
| buenos aires |
| patagonia    |
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                    |
|:------------------------|
| 2021-05-22 00:00:00.000 |
| 2021-05-23 00:00:00.000 |
| 2021-05-24 00:00:00.000 |
| 2021-05-25 00:00:00.000 |
| 2021-05-26 00:00:00.000 |
| 2021-05-27 00:00:00.000 |
| 2021-05-28 00:00:00.000 |
| 2021-05-29 00:00:00.000 |
| 2021-06-01 00:00:00.000 |
| 2021-06-02 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:
| product 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:
| work item type   |
|:-----------------|
| user story       |
| feature          |
| issue            |
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:
| notification_date       |
|:------------------------|
| 2020-03-11 00:00:00.000 |
| 2020-03-15 00:00:00.000 |
| 2020-03-16 00:00:00.000 |
| 2020-03-17 00:00:00.000 |
| 2020-03-18 00:00:00.000 |
| 2020-03-19 00:00:00.000 |
| 2020-03-20 00:00:00.000 |
| 2020-03-21 00:00:00.000 |
| 2020-03-22 00:00:00.000 |
| 2020-03-23 00:00:00.000 |
| 2020-03-24 00:00:00.000 |
| 2020-03-25 00:00:00.000 |
| 2020-03-26 00:00:00.000 |
| 2020-03-27 00:00:00.000 |
| 2020-03-28 00:00:00.000 |
| 2020-03-29 00:00:00.000 |
| 2020-03-30 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-04-01 00:00:00.000 |
| 2020-04-02 00:00:00.000 |
| 2020-04-03 00:00:00.000 |
| 2020-04-04 00:00:00.000 |
| 2020-04-05 00:00:00.000 |
| 2020-04-06 00:00:00.000 |
| 2020-04-07 00:00:00.000 |
| 2020-04-08 00:00:00.000 |
| 2020-03-09 00:00:00.000 |
| 2020-03-12 00:00:00.000 |
| 2020-03-13 00:00:00.000 |
| 2020-03-14 00:00:00.000 |
| 2020-03-10 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| district   |
|:-----------|
| chikwawa   |
| chiradzulu |
| chitipa    |
| dedza      |
| dowa       |
| karonga    |
| kasungu    |
| likoma     |
| lilongwe   |
| machinga   |
| mangochi   |
| mchinji    |
| mulanje    |
| mwanza     |
| mzimba     |
| neno       |
| nkhata bay |
| nkhotakota |
| nsanje     |
| ntcheu     |
| ntchisi    |
| phalombe   |
| rumphi     |
| salima     |
| thyolo     |
| zomba      |
| balaka     |
| blantyre   |
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:
| 申報起始日              |
|:------------------------|
| 2015-07-01 00:00:00.000 |
| 2014-07-01 00:00:00.000 |
| 2013-07-01 00:00:00.000 |
| 2015-01-01 00:00:00.000 |
| 2016-04-01 00:00:00.000 |
| 2013-01-01 00:00:00.000 |
| 2013-10-01 00:00:00.000 |
| 2016-01-01 00:00:00.000 |
| 2014-01-01 00:00:00.000 |
| 2015-10-01 00:00:00.000 |
| 2014-10-01 00:00:00.000 |
| 2013-04-01 00:00:00.000 |
| 2014-04-01 00:00:00.000 |
| 2016-01-31 00:00:00.000 |
| 2013-07-31 00:00:00.000 |
| 2013-12-01 00:00:00.000 |
| 2013-07-02 00:00:00.000 |
| 2016-02-25 00:00:00.000 |
| 2014-07-05 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:
| 許可年月日              |
|:------------------------|
| 1996-04-23 00:00:00.000 |
| 1997-07-10 00:00:00.000 |
| 2002-06-07 00:00:00.000 |
| 2000-03-29 00:00:00.000 |
| 2009-12-17 00:00:00.000 |
| 2010-04-01 00:00:00.000 |
| 2020-06-05 00:00:00.000 |
| 2014-09-18 00:00:00.000 |
| 2016-06-13 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:
| trimestre (format)   |
|:---------------------|
| trim3                |
| trim4                |
| trim2                |
| trim1                |
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:
| dtstart                 |
|:------------------------|
| 2018-11-01 00:00:00.000 |
| 2018-12-01 00:00:00.000 |
| 2019-01-01 00:00:00.000 |
| 2019-02-01 00:00:00.000 |
| 2019-03-01 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2019-05-01 00:00:00.000 |
| 2019-06-01 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:
| brand   |
|:--------|
| brand 2 |
| brand 3 |
| brand 4 |
| brand 1 |
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:
| lib_mod                       |
|:------------------------------|
| 2 000 à  4 999 habitants      |
| 5 000 à  9 999 habitants      |
| 10 000 à  19 999 habitants    |
| 20 000 à  49 999 habitants    |
| 50 000 à 99 999 habitants     |
| 100 000 à 199 999 habitants   |
| 200 000 à 1 999 999 habitants |
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:
| newvalue                               |
|:---------------------------------------|
| closed lost                            |
| ready for onboarding                   |
| contract stalled/escalated/on hold     |
| premier partner onboarding complete    |
| premier partner onboarding in progress |
| closed-company out of business         |
| contract executed                      |
| contract sent                          |
| location ready for onboarding          |
| onboarding complete                    |
| onboarding stalled/escalated/on hold   |
| stalled/on hold                        |
| closed-no longer needed for coverage   |
| closed-sold to another group           |
| in negotiations                        |
| initial contact                        |
| verbal agreement                       |
| prospect                               |
| ready for premier partner onboarding   |
| ready for onboarding - bnotes connect  |
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:
| dayofweekname   |
|:----------------|
| sunday          |
| monday          |
| tuesday         |
| wednesday       |
| thursday        |
| friday          |
| saturday        |
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:
| dealinginterface     |
|:---------------------|
| siena                |
| contact manager      |
| externaldealimporter |
| epayments            |
| etrader              |
| 360t super           |
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:
| productline      |
|:-----------------|
| classic cars     |
| trucks and buses |
| planes           |
| ships            |
| motorcycles      |
| vintage cars     |
| trains           |
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:
| товар.1              |
|:---------------------|
| масло сливочное      |
| мандарины фасованные |
| рис                  |
| сыр сырное царство   |
| батон белого         |