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:
| reviewer name       |
|:--------------------|
| brown, dalayzia     |
| coleman, shaniqua   |
| davis, portia       |
| johnson, enjoli     |
| lewis, brian        |
| tsyed               |
| washington, vatesta |
| weiss, phillip      |
| blue, connie        |
| rawls, darla        |
| wiard, warren       |
| blackbox, titus     |
| marlow, gayle       |
| watts, teresa       |
| howard, theresa     |
| eastin, denise      |
| teal, glynda        |
| postman, maria      |
| boulden, amanda     |
| cain, leah          |
| hunter, terri       |
| whelan, jeffrey     |
| kyser, jenny        |
| fleming, dyani      |
| daigh, beverly      |
| le, kinh            |
| saelens, janet      |
| taylor, judy        |
| bacon, fumei        |
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:
|   floor   |
|:----------|
| floor5    |
| floor11   |
| floor15   |
| floor1    |
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:
| location.admin.code   |
|:----------------------|
| iq-d087               |
| iq-d106               |
| iq-d088               |
| iq-d006               |
| iq-d002               |
| iq-d074               |
| iq-d085               |
| iq-d083               |
| iq-d066               |
| iq-d108               |
| iq-d041               |
| iq-d064               |
| iq-d038               |
| iq-d039               |
| iq-d073               |
| iq-d007               |
| iq-d008               |
| iq-d005               |
| iq-d045               |
| iq-d090               |
| iq-d049               |
| iq-d001               |
| iq-d101               |
| iq-d076               |
| iq-d075               |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| month year   |
|:-------------|
| jan 2020     |
| feb 2020     |
| mar 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:
| description                          |
|:-------------------------------------|
| department directors & managers      |
| human resources dept for all offices |
| technical / software dev team        |
| sales office in dallas, texas        |
| sales office in nyc                  |
| sales office in oxford               |
| sales in vancouver                   |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| tipo producto   |
|:----------------|
| accesorios      |
| aplicaciones    |
| celulares       |
| musica          |
| pc              |
| tablets         |
| tv              |
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:
| moeda   |
|:--------|
| cad     |
| gbp     |
| jpy     |
| usd     |
| eur     |
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:
| 品牌名称   |
|:-----------|
| 00004-干   |
| 00025-友   |
| 00011-冻   |
| 00026-佳   |
| 00027-事   |
| 00016-芙   |
| 00048-华   |
| 00043-妙   |
| 00013-茶   |
| 00039-门   |
| 00012-苔   |
| 00044-纺   |
| 00006-迈   |
| 00045-士   |
| 00014-间   |
| 00046-扬   |
| 00019-箭   |
| 00023-达   |
| 00024-罗   |
| 00047-芬   |
| 00015-鹤   |
| 00056-猛   |
| 00036-龙   |
| 00055-雅   |
| 00059-家   |
| 00063-达   |
| 00007-糖   |
| 00008-饮   |
| 00009-利   |
| 00010-卡   |
| 00054-蕾   |
| 00030-奇   |
| 00017-糖   |
| 00018-5    |
| 00038-阳   |
| 00029-场   |
| 00032-桥   |
| 00037-糖   |
| 00040-粮   |
| 00041-粮   |
| 00042-粮   |
| 00067-品   |
| 00079-斯   |
| 00053-涛   |
| 00058-丽   |
| 00020-爆   |
| 00080-球   |
| 00081-酒   |
| 00049-莲   |
| 00051-宝   |
| 00066-品   |
| 00083-沙   |
| 00033-村   |
| 00060-鸟   |
| 00021-大   |
| 00084-娘   |
| 00072-口   |
| 00085-源   |
| 00065-健   |
| 00087-品   |
| 00088-球   |
| 00089-子   |
| 00091-家   |
| 00061-伟   |
| 00092-江   |
| 00001-岛   |
| 00062-鸡   |
| 00093-芙   |
| 00094-装   |
| 00099-夫   |
| 00096-酥   |
| 00095-干   |
| 00100-巧   |
| 00057-珠   |
| 00082-诺   |
| 00064-敌   |
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:
| startdate           |
|:--------------------|
| 29/05/2019 03:00:00 |
| 04/06/2019 03:00:00 |
| 13/06/2019 03:00:00 |
| 05/06/2019 03:00:00 |
| 20/05/2019 03:00:00 |
| 19/06/2019 03:00:00 |
| 10/06/2019 03:00:00 |
| 17/06/2019 03:00:00 |
| 18/06/2019 03:00:00 |
| 24/06/2019 03:00:00 |
| 06/06/2019 03:00:00 |
| 21/06/2019 03:00:00 |
| 25/06/2019 03:00:00 |
| 12/06/2019 03:00:00 |
| 26/06/2019 03:00:00 |
| 27/06/2019 12:00:00 |
| 27/06/2019 14:00:00 |
| 27/06/2019 17:00:00 |
| 27/06/2019 20:00:00 |
| 28/06/2019 12:00:00 |
| 27/06/2019 19:00:00 |
| 28/06/2019 03:00:00 |
| 04/07/2019 13:00:00 |
| 08/07/2019 12:00:00 |
| 08/07/2019 17:00:00 |
| 10/07/2019 15:00:00 |
| 10/07/2019 14:00:00 |
| 10/07/2019 12:00:00 |
| 10/07/2019 18:00:00 |
| 11/07/2019 10:00:00 |
| 11/07/2019 13:00:00 |
| 11/07/2019 17:00:00 |
| 11/07/2019 16:00:00 |
| 11/07/2019 16:30:00 |
| 12/07/2019 10:00:00 |
| 12/07/2019 17:00:00 |
| 15/07/2019 12:38:20 |
| 15/07/2019 11:30:00 |
| 15/07/2019 20:30:00 |
| 15/07/2019 19:00:00 |
| 17/07/2019 10:30:00 |
| 17/07/2019 05:00:00 |
| 17/07/2019 12:00:00 |
| 16/07/2019 17:00:00 |
| 16/07/2019 10:00:00 |
| 18/07/2019 11:00:00 |
| 18/07/2019 19:00:00 |
| 19/07/2019 11:00:00 |
| 19/07/2019 12:00:00 |
| 18/07/2019 17:00:00 |
| 19/07/2019 13:30:00 |
| 19/07/2019 17:00:00 |
| 19/07/2019 19:00:00 |
| 22/07/2019 11:00:00 |
| 22/07/2019 12:00:00 |
| 22/07/2019 12:30:00 |
| 22/07/2019 19:00:00 |
| 22/07/2019 17:30:00 |
| 22/07/2019 20:00:00 |
| 23/07/2019 12:00:00 |
| 23/07/2019 03:00:00 |
| 24/07/2019 17:00:00 |
| 23/07/2019 17:00:00 |
| 23/07/2019 20:30:00 |
| 24/07/2019 12:00:00 |
| 24/07/2019 03:00:00 |
| 24/07/2019 19:00:00 |
| 25/07/2019 11:00:00 |
| 25/07/2019 13:00:00 |
| 26/07/2019 03:00:00 |
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:
| магазин   |
|:----------|
| тт4       |
| тт5       |
| тт6       |
| тт7       |
| тт8       |
| тт9       |
| тт10      |
| тт11      |
| тт12      |
| тт13      |
| тт2       |
| тт3       |
| тт1       |
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 of year           |
|:------------------------|
| 2020-01-01 00:00:00.000 |
| 2021-01-01 00:00:00.000 |
| 2022-01-01 00:00:00.000 |
| 2023-01-01 00:00:00.000 |
| 2024-01-01 00:00:00.000 |
| 2025-01-01 00:00:00.000 |
| 2026-01-01 00:00:00.000 |
| 2027-01-01 00:00:00.000 |
| 2028-01-01 00:00:00.000 |
| 2029-01-01 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| monthname   |
|:------------|
| april       |
| july        |
| october     |
| january     |
| february    |
| november    |
| august      |
| may         |
| march       |
| december    |
| june        |
| september   |
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:
| dealtype         |
|:-----------------|
| share issue      |
| share purchase   |
| insurance policy |
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:
| capital        |
|:---------------|
| goiânia        |
| aracajú        |
| são paulo      |
| florianópolis  |
| boa vista      |
| porto velho    |
| porto alegre   |
| natal          |
| rio de janeiro |
| terezina       |
| curitiba       |
| joão pessoa    |
| belém          |
| belo horizonte |
| cuiabá         |
| são luiz       |
| campo grande   |
| brasilia       |
| fortaleza      |
| vitória        |
| salvador       |
| manaus         |
| macapá         |
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:
| iso quarter   |
|:--------------|
| q3            |
| q4            |
| q2            |
| 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:
| change    |
|:----------|
| review    |
| no change |
| new       |
| updated   |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| nome                                  |
|:--------------------------------------|
| grupo padrão                          |
| vistoria técnica de poda de árvore    |
| secretaria do verde e meio ambiente   |
| mogi das cruzes                       |
| abertura de processos - semma         |
| gerencia de recursos naturais – semma |
| serviço de poda                       |
| vistoria de poda 2                    |
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:
| column2    |
|:-----------|
| 10/28/2019 |
| 9/16/2019  |
| 9/26/2019  |
| 10/18/2019 |
| 9/17/2019  |
| 9/20/2019  |
| 9/19/2019  |
| 9/24/2019  |
| 9/25/2019  |
| 9/30/2019  |
| 10/21/2019 |
| 9/27/2019  |
| 10/7/2019  |
| 10/9/2019  |
| 10/10/2019 |
| 10/15/2019 |
| 10/14/2019 |
| 10/17/2019 |
| 10/16/2019 |
| 11/6/2019  |
| 10/22/2019 |
| 10/30/2019 |
| 10/29/2019 |
| 11/8/2019  |
| 11/15/2019 |
| 11/4/2019  |
| 12/4/2019  |
| 11/14/2019 |
| 11/18/2019 |
| 11/5/2019  |
| 11/11/2019 |
| 2/7/2020   |
| 11/26/2019 |
| 11/27/2019 |
| 11/12/2019 |
| 11/19/2019 |
| 11/25/2019 |
| 12/3/2019  |
| 11/20/2019 |
| 12/18/2019 |
| 11/21/2019 |
| 11/28/2019 |
| 12/10/2019 |
| 9/18/2019  |
| 10/24/2019 |
| 10/8/2019  |
| 10/11/2019 |
| 10/23/2019 |
| 10/31/2019 |
| 11/7/2019  |
| 11/13/2019 |
| 12/2/2019  |
| 11/22/2019 |
| 11/1/2019  |
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:
| ngày                    |
|:------------------------|
| 2021-05-10 00:00:00.000 |
| 2021-05-17 00:00:00.000 |
| 2021-05-11 00:00:00.000 |
| 2021-05-18 00:00:00.000 |
| 2021-05-08 00:00:00.000 |
| 2021-05-04 00:00:00.000 |
| 2021-05-15 00:00:00.000 |
| 2021-05-06 00:00:00.000 |
| 2021-05-05 00:00:00.000 |
| 2021-05-12 00:00:00.000 |
| 2021-05-19 00:00:00.000 |
| 2021-05-07 00:00:00.000 |
| 2021-05-14 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| brand           |
|:----------------|
| savlon          |
| camel           |
| cuzy            |
| dettol          |
| madar           |
| rosatol         |
| cf              |
| harpic          |
| mr muscle       |
| queen elizabeth |
| sunlight        |
| nivea           |
| vaseline        |
| clinic clear    |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| segment        |
|:---------------|
| marketing      |
| management     |
| hq             |
| it             |
| finance        |
| hr             |
| field services |
| field sales    |
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           |
|:---------------|
| pricing amount |
| base mmc       |
| sios           |
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:
| schdate                 |
|:------------------------|
| 2020-07-02 00:00:00.000 |
| 2020-08-02 00:00:00.000 |
| 2020-09-02 00:00:00.000 |
| 2020-09-07 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:
| salesresponsible   |
|:-------------------|
| sd7                |
| sd2                |
| sd1                |
| sd4                |
| sd5                |
| sd3                |
| sd6                |
| sd8                |
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 of year           |
|:------------------------|
| 2021-01-01 00:00:00.000 |
| 2022-01-01 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| limitorderstatus   |
|:-------------------|
| active             |
| passive            |
| expired            |
| executed           |
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 line   |
|:---------------|
| s              |
| m              |
| r              |
| t              |
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:
| branch   |
|:---------|
| z        |
| q        |
| w        |
| e        |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| vendor                       |
|:-----------------------------|
| value of unsold inventory    |
| the andersons                |
| gangwish seed farms inc.     |
| rpm trucking inc             |
| cornerstone bank             |
| nau country insurance compan |
| adjustment for crop share    |
| fsa buffalo county           |
| fsa kearney county           |
| widdowson george             |
| d & l riley farms ltd        |
| central states helicopter co |
| box elder creek farm         |
| kaapa ethanol ravenna llc    |
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:
| attribute   |
|:------------|
| sigma       |
| statistic   |
| p.value     |
| df          |
| loglik      |
| aic         |
| bic         |
| deviance    |
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:
| price   |
|:--------|
| $3      |
| $9      |
| $22     |
| $14     |
| $7      |
| $16     |
| $2      |
| $15     |
| $69     |
| $32     |
| $6      |
| $19     |
| $4      |
| $1      |
| $0      |
| $11     |
| $13     |
| $21     |
| $35     |
| $38     |
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:
| clarity   |
|:----------|
| vs2       |
| si1       |
| vs1       |
| si2       |
| vvs2      |
| vvs1      |
| if        |
| i1        |
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 type   |
|:---------------|
| herbal tea     |
| tea            |
| espresso       |
| coffee         |
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:
| division           |
|:-------------------|
| mountain           |
| west south central |
| pacific            |
| new england        |
| south atlantic     |
| east north central |
| west north central |
| east south central |
| middle atlantic    |
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:
| jobcode_1        |
|:-----------------|
| lunch break      |
| cgar             |
| 1. company tasks |
| 2. service calls |
| health           |
| sick             |
| annual leave     |
| corporate        |
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:
| intent    |
|:----------|
| uninstall |
| required  |
| available |
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:
| process_speed   |
|:----------------|
| tidak tersedia  |
| cepat           |
| sangat cepat    |
| sedang          |
| lambat          |
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:
| sgpartido     |
|:--------------|
| pl            |
| dem           |
| mdb           |
| psc           |
| pros          |
| pp            |
| solidariedade |
| pt            |
| pr            |
| prb           |
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 name   |
|:-----------|
| thursday   |
| sunday     |
| tuesday    |
| friday     |
| wednesday  |
| saturday   |
| monday     |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| aeronave_operador_categoria   |
|:------------------------------|
| operador de aeronave          |
| operador particular           |
| escola de aviação             |
| fabricante aeronáutico        |
| órgão público                 |
| aeroclube                     |