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:
| date                    |
|:------------------------|
| 2021-06-03 00:00:00.000 |
| 2021-06-04 00:00:00.000 |
| 2021-06-05 00:00:00.000 |
| 2021-06-06 00:00:00.000 |
| 2021-06-07 00:00:00.000 |
| 2021-06-08 00:00:00.000 |
| 2021-06-09 00:00:00.000 |
| 2021-06-10 00:00:00.000 |
| 2021-06-11 00:00:00.000 |
| 2021-06-12 00:00:00.000 |
| 2021-06-13 00:00:00.000 |
| 2021-06-14 00:00:00.000 |
| 2021-06-15 00:00:00.000 |
| 2021-06-16 00:00:00.000 |
| 2021-06-17 00:00:00.000 |
| 2021-06-18 00:00:00.000 |
| 2021-06-19 00:00:00.000 |
| 2021-06-20 00:00:00.000 |
| 2021-06-21 00:00:00.000 |
| 2021-06-22 00:00:00.000 |
| 2021-06-23 00:00:00.000 |
| 2021-06-24 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:
| job title                 |
|:--------------------------|
| aero-structure technician |
| team leader               |
| lead technician           |
| production support        |
| quality inspector         |
| technician                |
| operator                  |
| tfw                       |
| cnc technician            |
| metrology lead technician |
| ndt inspector level 2     |
| painter                   |
| polisher                  |
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:
| z674048        |
|:---------------|
| dato irregular |
| dato correcto  |
| sin registro   |
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:
| fiscal quarter   |
|:-----------------|
| q2               |
| q3               |
| q4               |
| q1               |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| first name   |
|:-------------|
| firstname3   |
| firstname4   |
| firstname5   |
| firstname6   |
| firstname7   |
| firstname8   |
| firstname9   |
| firstname10  |
| firstname11  |
| firstname12  |
| firstname13  |
| firstname14  |
| firstname15  |
| firstname16  |
| firstname17  |
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:
| cityowner      |
|:---------------|
| goodlettsville |
| whites creek   |
| old hickory    |
| madison        |
| hermitage      |
| antioch        |
| brentwood      |
| mount juliet   |
| nashville      |
| nolensville    |
| bellevue       |
| joelton        |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| name   |
|:-------|
| c      |
| d      |
| e      |
| f      |
| g      |
| h      |
| i      |
| j      |
| k      |
| m      |
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 long name   |
|:------------------|
| january           |
| february          |
| march             |
| april             |
| may               |
| june              |
| july              |
| august            |
| september         |
| october           |
| november          |
| december          |
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:
| salesperson    |
|:---------------|
| jane stevens   |
| michelle smith |
| chris miller   |
| mike adams     |
| john jones     |
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:
| genre            |
|:-----------------|
| platform         |
| sports           |
| simulation       |
| action           |
| misc             |
| adventure        |
| shooter          |
| strategy         |
| role-playing     |
| puzzle           |
| visual novel     |
| action-adventure |
| music            |
| racing           |
| fighting         |
| mmo              |
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 type   |
|:-------------|
| warehouse    |
| comp inv     |
| ano inv      |
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:
| supplier   |
|:-----------|
| bar01      |
| pro01      |
| edf01      |
| blu01      |
| ast01      |
| goo01      |
| suz01      |
| tru01      |
| mag01      |
| cro01      |
| ref01      |
| dew01      |
| key01      |
| all01      |
| 02001      |
| ese01      |
| wes01      |
| bel01      |
| b&q01      |
| pos01      |
| wil01      |
| mcc01      |
| mis01      |
| gco01      |
| csl01      |
| gra01      |
| pbb01      |
| ser01      |
| dav01      |
| nwv01      |
| gdl01      |
| rsc01      |
| dls01      |
| shi01      |
| vik01      |
| con01      |
| ecco1      |
| hey01      |
| dac01      |
| jaw01      |
| phh01      |
| hal01      |
| tas01      |
| scr01      |
| opu01      |
| 34s01      |
| tea01      |
| rem01      |
| 99901      |
| mfl01      |
| inn01      |
| pen01      |
| amd01      |
| pcw01      |
| ton01      |
| fca01      |
| bac01      |
| ips01      |
| hen01      |
| she01      |
| bbh01      |
| wel01      |
| gah01      |
| hom01      |
| tsn01      |
| mid01      |
| tra01      |
| sam01      |
| cos01      |
| sho01      |
| hol01      |
| pre01      |
| har01      |
| arc01      |
| sai01      |
| zor01      |
| ama01      |
| far01      |
| sto01      |
| map01      |
| ukg01      |
| off01      |
| yel01      |
| gam02      |
| btb01      |
| hal02      |
| aer01      |
| rym01      |
| pri01      |
| hpi01      |
| cam01      |
| gam01      |
| boo01      |
| hea01      |
| nov01      |
| fim01      |
| c3kho      |
| kit01      |
| tam01      |
| cpc01      |
| dpt01      |
| rlm01      |
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   |
|:-----------|
| amordegrc  |
| amorlinc   |
| coupdaybs  |
| coupdays   |
| coupdaysnc |
| coupncd    |
| coupnum    |
| couppcd    |
| cumipmt    |
| cumprinc   |
| db         |
| ddb        |
| disc       |
| dollarde   |
| dollarfr   |
| duration   |
| effect     |
| fv         |
| intrate    |
| ipmt       |
| ispmt      |
| mduration  |
| nominal    |
| nper       |
| oddfprice  |
| oddfyield  |
| oddlprice  |
| oddlyield  |
| pduration  |
| pmt        |
| ppmt       |
| price      |
| pricedisc  |
| pricemat   |
| pv         |
| rate       |
| received   |
| rri        |
| sln        |
| syd        |
| tbilleq    |
| tbillprice |
| tbillyield |
| vdb        |
| xirr       |
| xnpv       |
| yield      |
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:
| base curr   |
|:------------|
| chf         |
| czk         |
| eur         |
| pln         |
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:
| construction        |
|:--------------------|
| wood                |
| masonry             |
| reinforced concrete |
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:
| capacitacao                     |
|:--------------------------------|
| conselheiros                    |
| gestor                          |
| trabalhadores de nível médio    |
| trabalhadores de nível superior |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| data.column1.question.metric   |
|:-------------------------------|
| csat                           |
| custom                         |
| nps                            |
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:
| datatype    |
|:------------|
| growth      |
| opportunity |
| strategy    |
| stability   |
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:
| tran date               |
|:------------------------|
| 2018-09-01 00:00:00.000 |
| 2018-06-01 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2018-03-01 00:00:00.000 |
| 2018-04-01 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-07-01 00:00:00.000 |
| 2018-08-01 00:00:00.000 |
| 2018-10-01 00:00:00.000 |
| 2018-11-01 00:00:00.000 |
| 2018-12-01 00:00:00.000 |
| 2018-02-01 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 |
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:
| games       |
|:------------|
| 2014 winter |
| 1948 summer |
| 1952 summer |
| 2008 summer |
| 1992 winter |
| 1994 winter |
| 2002 winter |
| 2006 winter |
| 1996 summer |
| 1952 winter |
| 1960 winter |
| 1912 summer |
| 2016 summer |
| 2000 summer |
| 1988 summer |
| 2012 summer |
| 1956 summer |
| 2004 summer |
| 1992 summer |
| 1936 summer |
| 1984 summer |
| 1928 summer |
| 1932 summer |
| 1980 summer |
| 1924 winter |
| 1904 summer |
| 2010 winter |
| 1964 summer |
| 1906 summer |
| 1960 summer |
| 1972 summer |
| 1968 winter |
| 1924 summer |
| 1988 winter |
| 1928 winter |
| 1908 summer |
| 1976 summer |
| 1972 winter |
| 1980 winter |
| 1984 winter |
| 1968 summer |
| 1936 winter |
| 1998 winter |
| 1956 winter |
| 1976 winter |
| 1964 winter |
| 1948 winter |
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       |
|:-----------|
| 03/01/2020 |
| 04/01/2020 |
| 05/01/2020 |
| 06/01/2020 |
| 07/01/2020 |
| 08/01/2020 |
| 09/01/2020 |
| 10/01/2020 |
| 16/02/2020 |
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:
| server          |
|:----------------|
| sql2005ser2003  |
| sql2008ser12r2  |
| sql2012ser08ag1 |
| sql2012ser08ag2 |
| sql2012ser08ag3 |
| sql2014ser12r2  |
| sql2016n1       |
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:
| date2                   |
|:------------------------|
| 2019-03-29 00:00:00.000 |
| 2019-06-29 00:00:00.000 |
| 2019-11-05 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:
| year                    |
|:------------------------|
| 2016-12-31 00:00:00.000 |
| 2017-12-31 00:00:00.000 |
| 2018-12-31 00:00:00.000 |
| 2019-12-31 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| column8                    |
|:---------------------------|
| 5                          |
| 6                          |
| rudy gobert                |
| positive trend             |
| boston                     |
| utah                       |
| milwaukee                  |
| james harden               |
| lebron james               |
| giannis antetokounmpo      |
| kawhi leonard              |
| best offensive player 2017 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| sku   |
|:------|
| sku1  |
| sku2  |
| sku3  |
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:
| number        |
|:--------------|
| sctask0401818 |
| inc0497913    |
| inc0341013    |
| inc0506568    |
| inc0437750    |
| sctask0442917 |
| inc0469919    |
| sctask0373899 |
| sctask0456732 |
| inc0485559    |
| sctask0422895 |
| sctask0523448 |
| sctask0432309 |
| inc0392819    |
| inc0326607    |
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:
| disposition                                                     |
|:----------------------------------------------------------------|
| treated and admitted for hospitalization (within same facility) |
| held for observation (includes admitted for observation)        |
| left without being seen/left against medical advice             |
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   |
|:-----------|
| lcm        |
| rcb        |
| cdm        |
| ldm        |
| st         |
| rb         |
| lcb        |
| lb         |
| cb         |
| rm         |
| cam        |
| cm         |
| rcm        |
| lm         |
| rdm        |
| lwb        |
| rwb        |
| lw         |
| rw         |
| ls         |
| rs         |
| cf         |
| gk         |
| lf         |
| ram        |
| lam        |
| rf         |
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:
| nombre                                   |
|:-----------------------------------------|
| román alonso garcía                      |
| grupo inmobiliario metropolitano s.a.    |
| sierra del guadarrama c.b.               |
| reabita obras de rehabilitación, s.l.    |
| administradores de fincas redfincas s.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:
| productcode   |
|:--------------|
| s10_4962      |
| s12_1099      |
| s12_1108      |
| s12_3148      |
| s12_3380      |
| s12_3891      |
| s12_3990      |
| s12_4675      |
| s18_1129      |
| s18_1589      |
| s18_1889      |
| s18_1984      |
| s18_2238      |
| s18_2870      |
| s18_3232      |
| s18_3278      |
| s18_3482      |
| s18_3685      |
| s18_4027      |
| s18_4721      |
| s18_4933      |
| s24_1046      |
| s24_1444      |
| s24_1628      |
| s24_2766      |
| s24_2840      |
| s24_2887      |
| s24_2972      |
| s24_3191      |
| s24_3371      |
| s24_3432      |
| s24_3856      |
| s24_4048      |
| s24_4620      |
| s700_2824     |
| s10_1678      |
| s10_2016      |
| s10_4698      |
| s12_2823      |
| s18_2625      |
| s18_3782      |
| s24_1578      |
| s24_2000      |
| s24_2360      |
| s32_1374      |
| s32_2206      |
| s32_4485      |
| s50_4713      |
| s18_1662      |
| s18_2581      |
| s24_1785      |
| s24_2841      |
| s24_3949      |
| s24_4278      |
| s700_1691     |
| s700_2466     |
| s700_2834     |
| s700_3167     |
| s700_4002     |
| s72_1253      |
| s18_3029      |
| s24_2011      |
| s700_1138     |
| s700_1938     |
| s700_2047     |
| s700_2610     |
| s700_3505     |
| s700_3962     |
| s72_3212      |
| s18_3259      |
| s32_3207      |
| s50_1514      |
| s12_1666      |
| s12_4473      |
| s18_1097      |
| s18_2319      |
| s18_2432      |
| s18_4600      |
| s24_2300      |
| s32_1268      |
| s32_2509      |
| s32_3522      |
| s50_1392      |
| s18_1342      |
| s18_1367      |
| s18_1749      |
| s18_2248      |
| s18_2325      |
| s18_2795      |
| s18_2949      |
| s18_2957      |
| s18_3136      |
| s18_3140      |
| s18_3320      |
| s18_3856      |
| s18_4409      |
| s18_4522      |
| s18_4668      |
| s24_1937      |
| s24_2022      |
| s24_3151      |
| s24_3420      |
| s24_3816      |
| s24_3969      |
| s24_4258      |
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:
| order priority   |
|:-----------------|
| medium           |
| low              |
| 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:
| month name   |
|:-------------|
| january      |
| february     |
| march        |
| april        |
| may          |
| june         |
| july         |
| august       |
| september    |
| october      |
| november     |
| december     |
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   |
|:----------|
| product2  |
| product3  |
| product1  |
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:
| vin               |
|:------------------|
| xxw0169zovk5yj41o |
| 5yhti7i7hugg0qau7 |
| 0iesvcf1kgkeoxyys |
| wfsgfjxh1ab67x93t |
| uv96m83qql7ved4yb |
| sz1plfqyjnudhyd3w |
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:
| d_source_code   |
|:----------------|
| acbk1           |
| pmcc1           |
| swch2           |
| pmcc1a          |
| swch1           |
| 32cl8t          |
| arqn1           |
| ariq1           |
| ch2123          |
| ccc1            |
| a2cc1           |
| a2cc2           |
| a2cc8           |
| a2cc4           |
| a2cc7           |
| ac2123          |
| acc2a           |
| 32a4o6s7        |
| acc1b           |
| a2cc1b          |
| 32a57bnc        |
| a2cc5b          |
| a2cc7b          |
| acc1a           |
| acc1            |
| acc2            |
| tesc01          |
| armn1           |
| ariq23          |
| a2cc3           |
| a2cc2a          |
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_time     |
|:-------------|
| evening_peak |
| morning_peak |
| midday       |
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:
| d7                   |
|:---------------------|
| $100,000 to $250,000 |
| prefer not to answer |
| $60,000 to $80,000   |
| $80,000 to $100,000  |
| $40,000 to $60,000   |
| $20,000 to $40,000   |
| less than $20,000    |
| more than $250,000   |
| 9                    |


label: 9
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:
| monthshortname   |
|:-----------------|
| jul              |
| aug              |
| sep              |
| oct              |
| nov              |
| dec              |
| jan              |
| feb              |
| mar              |
| apr              |
| may              |
| jun              |