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:
| id                           |
|:-----------------------------|
| exchange                     |
| osdpplatform                 |
| sharepoint                   |
| bookings                     |
| cloudappsecurity             |
| dynamicscrm                  |
| forms                        |
| intune                       |
| lync                         |
| mdatp                        |
| microsoftdefenderforidentity |
| microsoftflow                |
| microsoftflowm365            |
| microsoftteams               |
| mobiledevicemanagement       |
| o365client                   |
| officeonline                 |
| onedriveforbusiness          |
| orgliveid                    |
| pam                          |
| planner                      |
| powerapps                    |
| powerappsm365                |
| powerbicom                   |
| rms                          |
| staffhub                     |
| stream                       |
| swayenterprise               |
| yammer                       |
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:
| stage_name       |
|:-----------------|
| application      |
| review           |
| assessment       |
| first interview  |
| second interview |
| offer            |
| offer response   |
| start in job     |
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:
| priority   |
|:-----------|
| medium     |
| important  |
| low        |
| urgent     |
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:
| model          |
|:---------------|
| cd100          |
| mxi            |
| k9k            |
| b4a            |
| b57            |
| scooter        |
| b58            |
| yl7            |
| 1.5l           |
| eagle am       |
| p15 dost       |
| mdep           |
| b38            |
| npd            |
| ktc/p90        |
| ktna ve        |
| pleasure       |
| cd100 ve       |
| mdi-fes nef(3) |
| fes            |
| nef (3)        |
| mdi-fes        |
| nef (1)        |
| nef (4)        |
| nef (2)        |
| kvea           |
| mdi-as         |
| mhawk          |
| mhawk ve       |
| agni           |
| b4dhs          |
| n112           |
| s101           |
| new f8d        |
| aata           |
| pleasure ve    |
| aata/aanf ve   |
| aata/aanf      |
| p90 ve         |
| tgdi 1.2l      |
| d15 1.5l       |
| aaff           |
| abga           |
| d22 mhawk      |
| 800cc          |
| esteem         |
| euro iii       |
| euro iv        |
| al             |
| 800cc dicor    |
| spd kit        |
| indica         |
| 1.4 crail      |
| 2.2 ltr        |
| thermometric   |
| wego           |
| apache         |
| u162           |
| star           |
| ybx            |
| p40            |
| ktna           |
| f8d/f10d       |
| lci accent     |
| g13            |
| flame          |
| kappa          |
| kappa leaded   |
| model n        |
| 5l             |
| 3l             |
| p15 dost cng   |
| episilon/pa    |
| 2lp1           |
| nano cng       |
| 1gc            |
| e01 bike       |
| u177           |
| motorcycle     |
| vvt            |
| msil 2w        |
| e06 scooter    |
| e07            |
| u diesel       |
| e15            |
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:
| daysuffix   |
|:------------|
| th          |
| st          |
| nd          |
| rd          |
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:
| resposnible         |
|:--------------------|
| contractor          |
| aurora              |
| unknown             |
| others              |
| aurora & contractor |
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:
| 期中考週前第幾季上課/期末考週前第幾季上課   |
|:--------------------------------------------|
| 期末考週前第1季上課                         |
| 是期末考週                                  |
| 期中考週前第1季上課                         |
| 是期中考週                                  |
| 不是上課日                                  |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| country   |
|:----------|
| uk        |
| usa       |
| ru        |
| pl        |
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:
| dt_reason               |
|:------------------------|
| break                   |
| forklift                |
| equipment failure       |
| other                   |
| material unavailable    |
| non-conforming material |
| changed work function   |
| change over             |
| shift over              |
| meeting                 |
| ordered completed       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| dayofweek     |
|:--------------|
| sexta-feira   |
| sábado        |
| domingo       |
| segunda-feira |
| terça-feira   |
| quarta-feira  |
| quinta-feira  |
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:
| dt                      |
|:------------------------|
| 2012-03-06 00:00:00.000 |
| 2012-04-01 00:00:00.000 |
| 2012-04-06 00:00:00.000 |
| 2012-05-07 00:00:00.000 |
| 2013-01-01 00:00:00.000 |
| 2013-01-06 00:00:00.000 |
| 2013-02-01 00:00:00.000 |
| 2013-02-06 00:00:00.000 |
| 2013-03-01 00:00:00.000 |
| 2013-03-06 00:00:00.000 |
| 2013-04-01 00:00:00.000 |
| 2013-04-06 00:00:00.000 |
| 2013-05-03 00:00:00.000 |
| 2013-05-07 00:00:00.000 |
| 2013-11-25 00:00:00.000 |
| 2013-12-07 00:00:00.000 |
| 2013-06-11 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:
| 2021 team   |
|:------------|
| pbks        |
| csk         |
| dc          |
| kkr         |
| srh         |
| rcb         |
| mi          |
| rr          |
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:
| not_hourname    |
|:----------------|
| 4:00pm-4:59pm   |
| 10:00pm-10:59pm |
| 5:00pm-5:59pm   |
| 2:00pm-2:59pm   |
| 3:00pm-3:59pm   |
| 12:00pm-12:59pm |
| 9:00pm-9:59pm   |
| 1:00pm-1:59pm   |
| 11:00am-11:59am |
| 5:00am-5:59am   |
| 0:00am-0:59am   |
| 2:00am-2:59am   |
| 4:00am-4:59am   |
| 3:00am-3:59am   |
| 7:00pm-7:59pm   |
| 6:00pm-6:59pm   |
| 1:00am-1:59am   |
| 11:00pm-11:59pm |
| 8:00pm-8:59pm   |
| 9:00am-9:59am   |
| 7:00am-7:59am   |
| 6:00am-6:59am   |
| 10:00am-10:59am |
| 8:00am-8:59am   |
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:
| type2    |
|:---------|
| poison   |
| flying   |
| ground   |
| ice      |
| steel    |
| psychic  |
| rock     |
| fighting |
| ghost    |
| dark     |
| normal   |
| fire     |
| grass    |
| electric |
| dragon   |
| fairy    |
| water    |
| bug      |
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:
| updated-at              |
|:------------------------|
| 2021-02-25 13:32:10.000 |
| 2021-02-25 13:32:12.000 |
| 2021-02-25 13:32:13.000 |
| 2021-02-25 13:32:15.000 |
| 2021-02-25 13:32:17.000 |
| 2021-02-25 13:32:18.000 |
| 2021-02-25 13:32:20.000 |
| 2021-02-25 13:32:21.000 |
| 2021-02-25 13:32:22.000 |
| 2021-02-25 13:32:23.000 |
| 2021-02-25 13:32:24.000 |
| 2021-02-25 13:32:25.000 |
| 2021-02-25 13:32:26.000 |
| 2021-02-25 13:32:27.000 |
| 2020-10-26 11:40:16.000 |
| 2020-10-26 11:40:17.000 |
| 2021-02-25 13:32:28.000 |
| 2021-02-25 13:32:29.000 |
| 2021-02-25 13:32:30.000 |
| 2021-02-25 13:32:31.000 |
| 2021-02-25 13:32:32.000 |
| 2021-02-25 13:32:33.000 |
| 2021-02-25 13:32:34.000 |
| 2021-02-25 13:32:35.000 |
| 2021-02-25 13:32:37.000 |
| 2021-02-25 13:32:38.000 |
| 2021-02-25 13:32:39.000 |
| 2021-02-25 13:32:41.000 |
| 2021-02-25 13:32:42.000 |
| 2021-02-25 13:32:43.000 |
| 2021-02-25 13:32:44.000 |
| 2021-02-25 13:32:45.000 |
| 2021-02-25 13:32:46.000 |
| 2021-02-25 13:32:47.000 |
| 2021-02-25 13:32:48.000 |
| 2021-02-25 13:32:49.000 |
| 2021-02-25 13:32:50.000 |
| 2021-02-25 13:32:51.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| date                    |
|:------------------------|
| 2021-03-03 00:00:00.000 |
| 2021-03-04 00:00:00.000 |
| 2021-03-05 00:00:00.000 |
| 2021-03-06 00:00:00.000 |
| 2021-03-07 00:00:00.000 |
| 2021-03-08 00:00:00.000 |
| 2021-03-09 00:00:00.000 |
| 2021-03-10 00:00:00.000 |
| 2021-03-11 00:00:00.000 |
| 2021-03-12 00:00:00.000 |
| 2021-03-13 00:00:00.000 |
| 2021-03-14 00:00:00.000 |
| 2021-03-15 00:00:00.000 |
| 2021-03-16 00:00:00.000 |
| 2021-03-17 00:00:00.000 |
| 2021-03-18 00:00:00.000 |
| 2021-03-19 00:00:00.000 |
| 2021-03-20 00:00:00.000 |
| 2021-03-21 00:00:00.000 |
| 2021-03-22 00:00:00.000 |
| 2021-03-23 00:00:00.000 |
| 2021-03-24 00:00:00.000 |
| 2021-03-25 00:00:00.000 |
| 2021-03-26 00:00:00.000 |
| 2021-03-27 00:00:00.000 |
| 2021-03-28 00:00:00.000 |
| 2021-03-29 00:00:00.000 |
| 2021-03-30 00:00:00.000 |
| 2021-03-31 00:00:00.000 |
| 2021-04-01 00:00:00.000 |
| 2021-04-02 00:00:00.000 |
| 2021-04-03 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| eomonth                 |
|:------------------------|
| 2021-01-31 00:00:00.000 |
| 2021-02-28 00:00:00.000 |
| 2021-03-31 00:00:00.000 |
| 2021-04-30 00:00:00.000 |
| 2021-05-31 00:00:00.000 |
| 2021-06-30 00:00:00.000 |
| 2021-07-31 00:00:00.000 |
| 2021-08-31 00:00:00.000 |
| 2021-09-30 00:00:00.000 |
| 2021-10-31 00:00:00.000 |
| 2021-11-30 00:00:00.000 |
| 2021-12-31 00:00:00.000 |
| 2022-01-31 00:00:00.000 |
| 2022-02-28 00:00:00.000 |
| 2022-03-31 00:00:00.000 |
| 2022-04-30 00:00:00.000 |
| 2022-05-31 00:00:00.000 |
| 2022-06-30 00:00:00.000 |
| 2022-07-31 00:00:00.000 |
| 2022-08-31 00:00:00.000 |
| 2022-09-30 00:00:00.000 |
| 2022-10-31 00:00:00.000 |
| 2022-11-30 00:00:00.000 |
| 2022-12-31 00:00:00.000 |
| 2023-01-31 00:00:00.000 |
| 2023-02-28 00:00:00.000 |
| 2023-03-31 00:00:00.000 |
| 2023-04-30 00:00:00.000 |
| 2023-05-31 00:00:00.000 |
| 2023-06-30 00:00:00.000 |
| 2023-07-31 00:00:00.000 |
| 2023-08-31 00:00:00.000 |
| 2023-09-30 00:00:00.000 |
| 2023-10-31 00:00:00.000 |
| 2023-11-30 00:00:00.000 |
| 2023-12-31 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:
| categoryname   |
|:---------------|
| protein        |
| carbs          |
| clothing       |
| vitamins       |
| amino acids    |
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:
| new_contractno   |
|:-----------------|
| cnt-00001472     |
| cnt-00002450     |
| cnt-00002234     |
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:
| eventname                             |
|:--------------------------------------|
| sqlsaturday #733 - atlanta 2018       |
| sqlsaturday #734 - dallas 2018        |
| sqlsaturday #735 - finland 2018       |
| sqlsaturday #736 - columbus 2018      |
| sqlsaturday #738 - israel 2018        |
| sqlsaturday #739 - kyiv 2018          |
| sqlsaturday #740 - orange county 2018 |
| sqlsaturday #741 - albany 2018        |
| sqlsaturday #742 - cork 2018          |
| sqlsaturday #743 - pensacola 2018     |
| sqlsaturday #744 - caxias do sul 2018 |
| sqlsaturday #745 - indianapolis 2018  |
| sqlsaturday #747 - timisoara 2018     |
| sqlsaturday #749 - baton rouge 2018   |
| sqlsaturday #750 - sri lanka 2018     |
| sqlsaturday #751 - santiago 2018      |
| sqlsaturday #752 - iowa city 2018     |
| sqlsaturday #755 - south florida 2018 |
| sqlsaturday #756 - bafoussam 2018     |
| sqlsaturday #757 - sacramento 2018    |
| sqlsaturday #758 - montreal 2018      |
| sqlsaturday #759 - jacksonville 2018  |
| sqlsaturday #760 - rheinland 2018     |
| sqlsaturday #762 - paris 2018         |
| sqlsaturday #764 - bratislava 2018    |
| sqlsaturday #766 - houston 2018       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| start                   |
|:------------------------|
| 2020-06-01 08:14:41.000 |
| 2020-06-01 08:57:03.000 |
| 2020-06-01 09:16:00.000 |
| 2020-06-01 08:50:07.000 |
| 2020-06-01 08:54:39.000 |
| 2020-06-01 08:26:54.000 |
| 2020-06-01 08:14:43.000 |
| 2020-07-01 08:56:15.000 |
| 2020-07-01 09:11:14.000 |
| 2020-07-01 15:11:30.000 |
| 2020-07-01 08:56:16.000 |
| 2020-07-01 08:46:33.000 |
| 2020-07-01 08:55:17.000 |
| 2020-07-01 08:41:29.000 |
| 2020-07-01 09:00:26.000 |
| 2020-07-01 08:39:56.000 |
| 2020-07-01 08:53:27.000 |
| 2020-07-01 08:21:24.000 |
| 2020-07-01 07:40:00.000 |
| 2020-07-01 12:00:00.000 |
| 2020-07-01 18:00:00.000 |
| 2020-08-01 09:00:00.000 |
| 2020-08-01 14:05:08.000 |
| 2020-08-01 08:57:56.000 |
| 2020-08-01 08:49:08.000 |
| 2020-08-01 08:57:04.000 |
| 2020-08-01 08:49:14.000 |
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:
| bal sheet sub category                        |
|:----------------------------------------------|
| property, plant & equipment (ppe)             |
| trademarks                                    |
| franchise rights                              |
| goodwill                                      |
| equity method investments                     |
| other investments                             |
| other assets                                  |
| other intangible assets                       |
| deferred income tax assets                    |
| cash & cash equivalents                       |
| short-term investments                        |
| marketable securities                         |
| trade accounts receivables                    |
| inventories                                   |
| prepaid expenses & other assets               |
| assets held for sale                          |
| assets held for resale                        |
| accounts payable & accrued expenses           |
| loans & notes payable                         |
| current maturities of long-term debt          |
| accrued income taxes                          |
| liabilities held for sale                     |
| long term debt                                |
| other liabilities                             |
| deferred income tax liabilities               |
| common stock                                  |
| capital surplus                               |
| reinvested earnings                           |
| accumulated other comprehensive income (loss) |
| treasury stock                                |
| non-controlling interests                     |
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:
| dagvdweekomschr.   |
|:-------------------|
| wo                 |
| do                 |
| vr                 |
| za                 |
| zo                 |
| ma                 |
| di                 |
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         |
|:--------------|
| nike          |
| converse      |
| nike,converse |
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   |
|:--------|
| jul     |
| aug     |
| sep     |
| oct     |
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 end date           |
|:------------------------|
| 2015-12-31 00:00:00.000 |
| 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 |
| 2020-12-31 00:00:00.000 |
| 2021-12-31 00:00:00.000 |
| 2022-12-31 00:00:00.000 |
| 2023-12-31 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:
| idade_cta (groups)   |
|:---------------------|
| 101 a 200            |
| 13 a 24              |
| 00 a 04              |
| 51 a 80              |
| 25 a 50              |
| 81 a 100             |
| 05 a 08              |
| 09 a 12              |
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:
| mbx_storageused(kb/mb/gb/tb)   |
|:-------------------------------|
| 504.18 mb                      |
| 3.67 gb                        |
| 3.17 gb                        |
| 504.36 mb                      |
| 3.64 gb                        |
| 3.15 gb                        |
| 3.47 gb                        |
| 3.00 gb                        |
| 482.37 mb                      |
| 3.41 gb                        |
| 2.93 gb                        |
| 493.25 mb                      |
| 3.34 gb                        |
| 2.86 gb                        |
| 487.71 mb                      |
| 3.26 gb                        |
| 2.77 gb                        |
| 506.45 mb                      |
| 3.19 gb                        |
| 2.70 gb                        |
| 499.42 mb                      |
| 3.08 gb                        |
| 2.60 gb                        |
| 496.19 mb                      |
| 2.88 gb                        |
| 2.41 gb                        |
| 483.76 mb                      |
| 2.95 gb                        |
| 2.45 gb                        |
| 518.70 mb                      |
| 3.61 gb                        |
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:
| pbi_table_id                  |
|:------------------------------|
| vm-mivodtyhzkge.db.5.tbl.235  |
| vm-mivodtyhzkge.db.5.tbl.239  |
| vm-mivodtyhzkge.db.5.tbl.240  |
| vm-mivodtyhzkge.db.5.tbl.241  |
| vm-mivodtyhzkge.db.5.tbl.246  |
| vm-mivodtyhzkge.db.5.tbl.247  |
| vm-mivodtyhzkge.db.5.tbl.252  |
| vm-mivodtyhzkge.db.5.tbl.303  |
| vm-mivodtyhzkge.db.5.tbl.308  |
| vm-mivodtyhzkge.db.40.tbl.553 |
| vm-mivodtyhzkge.db.40.tbl.556 |
| vm-mivodtyhzkge.db.40.tbl.560 |
| vm-mivodtyhzkge.db.40.tbl.566 |
| vm-qvzuxjgpdtmr.db.5.tbl.236  |
| vm-qvzuxjgpdtmr.db.5.tbl.240  |
| vm-qvzuxjgpdtmr.db.5.tbl.241  |
| vm-qvzuxjgpdtmr.db.5.tbl.242  |
| vm-qvzuxjgpdtmr.db.5.tbl.243  |
| vm-qvzuxjgpdtmr.db.5.tbl.244  |
| vm-qvzuxjgpdtmr.db.5.tbl.247  |
| vm-qvzuxjgpdtmr.db.5.tbl.248  |
| vm-qvzuxjgpdtmr.db.5.tbl.250  |
| vm-qvzuxjgpdtmr.db.5.tbl.253  |
| vm-qvzuxjgpdtmr.db.5.tbl.302  |
| vm-qvzuxjgpdtmr.db.5.tbl.320  |
| vm-qvzuxjgpdtmr.db.39.tbl.437 |
| vm-qvzuxjgpdtmr.db.39.tbl.438 |
| vm-qvzuxjgpdtmr.db.39.tbl.443 |
| vm-qvzuxjgpdtmr.db.39.tbl.444 |
| vm-qvzuxjgpdtmr.db.39.tbl.445 |
| vm-qvzuxjgpdtmr.db.39.tbl.452 |
| vm-qvzuxjgpdtmr.db.39.tbl.454 |
| vm-qvzuxjgpdtmr.db.39.tbl.457 |
| vm-mivodtyhzkge.db.5.tbl.304  |
| vm-qvzuxjgpdtmr.db.5.tbl.322  |
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:
| salesprofitlevel   |
|:-------------------|
| low                |
| negative           |
| 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:
| eventunittype   |
|:----------------|
| none            |
| noc             |
| ath             |
| coup            |
| hteam           |
| hnoc            |
| team            |
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:
| rango        |
|:-------------|
| año          |
| cuatrimestre |
| mes          |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| account group                |
|:-----------------------------|
| revenue                      |
| cost of goods sold           |
| wages and payroll taxes      |
| rent                         |
| utilities                    |
| royalties                    |
| advertising                  |
| insurance                    |
| travel                       |
| other operating expenses     |
| professional fees            |
| other non operating expenses |
| benefits                     |
| gp                           |
| ida                          |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| agent 3   |
|:----------|
| g         |
| t         |
| w         |
| s         |
| m         |
| i         |
| e         |
| a         |
| p         |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| country     |
|:------------|
| germany     |
| austria     |
| france      |
| italy       |
| switzerland |
| liechstein  |


label: liechstein