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:
| yearmonthnumber   |
|:------------------|
| 2015/07           |
| 2016/07           |
| 2017/07           |
| 2018/07           |
| 2015/08           |
| 2016/08           |
| 2017/08           |
| 2018/08           |
| 2015/09           |
| 2016/09           |
| 2017/09           |
| 2018/09           |
| 2015/06           |
| 2016/06           |
| 2017/06           |
| 2018/06           |
| 2015/04           |
| 2016/04           |
| 2017/04           |
| 2018/04           |
| 2015/05           |
| 2016/05           |
| 2017/05           |
| 2018/05           |
| 2015/01           |
| 2016/01           |
| 2017/01           |
| 2018/01           |
| 2015/03           |
| 2016/03           |
| 2017/03           |
| 2018/03           |
| 2015/02           |
| 2016/02           |
| 2017/02           |
| 2018/02           |
| 2015/12           |
| 2016/12           |
| 2017/12           |
| 2018/12           |
| 2015/11           |
| 2016/11           |
| 2017/11           |
| 2018/11           |
| 2015/10           |
| 2016/10           |
| 2017/10           |
| 2018/10           |
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:
| time resolved           |
|:------------------------|
| 2018-08-23 10:50:00.000 |
| 2018-08-24 14:34:00.000 |
| 2018-08-08 15:49:00.000 |
| 2018-08-29 14:32:00.000 |
| 2018-08-29 12:14:00.000 |
| 2018-08-29 17:27:00.000 |
| 2018-08-02 13:02:00.000 |
| 2018-08-27 11:23:00.000 |
| 2018-08-15 14:28:00.000 |
| 2018-08-14 15:45:00.000 |
| 2018-08-14 17:05:00.000 |
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:
| sub-category (groups)   |
|:------------------------|
| supplier-1              |
| supplier-3              |
| supplier-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:
| name     |
|:---------|
| bitcoin  |
| cardano  |
| dogecoin |
| ethereum |
| tether   |
| xrp      |
| solana   |
| polkadot |
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                    |
|:------------------------|
| 2018-10-31 00:00:00.000 |
| 2018-11-30 00:00:00.000 |
| 2018-12-31 00:00:00.000 |
| 2019-01-31 00:00:00.000 |
| 2019-02-28 00:00:00.000 |
| 2019-03-31 00:00:00.000 |
| 2019-04-30 00:00:00.000 |
| 2019-05-31 00:00:00.000 |
| 2019-06-30 00:00:00.000 |
| 2019-07-31 00:00:00.000 |
| 2019-08-31 00:00:00.000 |
| 2019-09-30 00:00:00.000 |
| 2019-10-31 00:00:00.000 |
| 2019-11-30 00:00:00.000 |
| 2019-12-31 00:00:00.000 |
| 2020-01-31 00:00:00.000 |
| 2020-02-29 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-04-30 00:00:00.000 |
| 2020-05-31 00:00:00.000 |
| 2020-06-30 00:00:00.000 |
| 2020-07-31 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:
| fecha de término        |
|:------------------------|
| 2018-12-31 00:00:00.000 |
| 2018-09-30 00:00:00.000 |
| 2018-02-28 00:00:00.000 |
| 2018-03-31 00:00:00.000 |
| 2018-07-31 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2018-01-31 00:00:00.000 |
| 2018-11-30 00:00:00.000 |
| 2018-04-30 00:00:00.000 |
| 2018-06-30 00:00:00.000 |
| 2018-08-31 00:00:00.000 |
| 2018-10-31 00:00:00.000 |
| 2018-05-31 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:
| date                    |
|:------------------------|
| 2022-04-03 00:00:00.000 |
| 2022-04-04 00:00:00.000 |
| 2022-04-05 00:00:00.000 |
| 2022-04-06 00:00:00.000 |
| 2022-04-07 00:00:00.000 |
| 2022-04-08 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:
| division     |
|:-------------|
| cereals      |
| baking goods |
| grain snacks |
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:
| site code   |
|:------------|
| caz         |
| prz         |
| sez         |
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:
| junction detail   |
|:------------------|
| 0 no jun in 20m   |
| 6 crossroads      |
| 3 t/stag jun      |
| 9 other jun       |
| 8 priv drive      |
| 1 roundabout      |
| 7 multi jun       |
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:
| fy   |
|:-----|
| fy20 |
| fy21 |
| fy22 |
| fy23 |
| fy19 |
| fy17 |
| fy18 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| nombre                |
|:----------------------|
| juan vega             |
| nestor lupayante      |
| nelson salinas        |
| williams pizarro      |
| sebastian orellana    |
| migual salinas        |
| peper rojas           |
| juan perez            |
| jorgue morris         |
| miguel cantillana     |
| jorge pinto           |
| jorge zosa            |
| jose miguel corriente |
| juan castillo         |
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:
| race                               |
|:-----------------------------------|
| black - african american           |
| asian                              |
| other                              |
| native hawaiian - pacific islander |
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   |
|:-----------|
| fri        |
| sat        |
| sun        |
| mon        |
| tue        |
| wed        |
| thu        |
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:
| surname   |
|:----------|
| kim       |
| branson   |
| krylova   |
| voronoff  |
| desk      |
| arner     |
| mattos    |
| ?????´??  |
| jones     |


label: ?????´??
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_sale                 |
|:------------------------|
| 2013-03-01 00:00:00.000 |
| 2013-04-01 00:00:00.000 |
| 2012-02-06 00:00:00.000 |
| 2013-01-06 00:00:00.000 |
| 2013-02-06 00:00:00.000 |
| 2013-03-06 00:00:00.000 |
| 2013-04-06 00:00:00.000 |
| 2012-04-06 00:00:00.000 |
| 2012-01-06 00:00:00.000 |
| 2012-03-06 00:00:00.000 |
| 2013-06-11 00:00:00.000 |
| 2012-04-01 00:00:00.000 |
| 2013-01-01 00:00:00.000 |
| 2013-02-01 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:
| sending agent code   |
|:---------------------|
| chiimpytn            |
| cenglobhx            |
| atmint_ae            |
| minwaraejea          |
| phiair_ph            |
| agentsha             |
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:
| middlename   |
|:-------------|
| d            |
| p.           |
| m.           |
| c.           |
| kate         |
| marie        |
| b            |
| a            |
| armand       |
| a.           |
| l.           |
| albert       |
| anthony      |
| c            |
| keith        |
| john         |
| lynn         |
| richard      |
| g            |
| james        |
| middle       |
| e            |
| k            |
| j.           |
| willy        |
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:
| activitytype   |
|:---------------|
| search         |
| opened         |
| comment        |
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:
| area_name                 |
|:--------------------------|
| manipur                   |
| tripura                   |
| mizoram                   |
| sikkim                    |
| goa                       |
| arunachal pradesh         |
| puducherry                |
| chandigarh                |
| andaman & nicobar islands |
| dadra & nagar haveli      |
| daman & diu               |
| nagaland                  |
| west bengal               |
| lakshadweep               |
| odisha                    |
| andhra pradesh            |
| uttar pradesh             |
| jharkhand                 |
| punjab                    |
| tamil nadu                |
| karnataka                 |
| haryana                   |
| jammu & kashmir           |
| meghalaya                 |
| himachal pradesh          |
| uttarakhand               |
| madhya pradesh            |
| assam                     |
| bihar                     |
| maharashtra               |
| rajasthan                 |
| chhattisgarh              |
| delhi                     |
| kerala                    |
| gujarat                   |
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:
| línea de actuación   |
|:---------------------|
| hito 2               |
| hito 3               |
| hito 4               |
| hito 6               |
| hito 7               |
| hito 8               |
| hito 9               |
| hito 10              |
| hito 11              |
| hito 12              |
| hito 13              |
| hito 14              |
| hito 15              |
| hito 16              |
| hito 17              |
| hito 18              |
| hito 19              |
| hito 20              |
| hito 21              |
| hito 22              |
| hito 23              |
| hito 24              |
| hito 25              |
| hito 26              |
| hito 27              |
| hito 28              |
| hito 29              |
| hito 30              |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| sales category 1   |
|:-------------------|
| σφολιατεσ          |
| γλυκα              |
| σοκολατα           |
| pizza              |
| muffin_donut       |
| σαλατεσ            |
| σαντουιτσ          |
| βαφλα              |
| burger             |
| πατατεσ            |
| club               |
| κρεπεσ γλυκιεσ     |
| κρεπα_μιση μιση    |
| ντιπακια           |
| hotdog             |
| κρεπεσ αλμυρεσ     |
| τοστ               |
| νηστησιμα          |
| χυμοι              |
| αναψυκτικα         |
| γαλα               |
| κρυα ροφηματα      |
| ζεστα ροφηματα     |
| μπυρεσ             |
| σοκολατα special   |
| λοιπα              |
| γρανιτεσ           |
| νερα               |
| κρασια             |
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:
| loser2     |
|:-----------|
| qalandars  |
| gladiators |
| sultans    |
| kings      |
| zalmi      |
| united     |
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:
| potential opportunities owner   |
|:--------------------------------|
| dave goss                       |
| david miley                     |
| emily rudin                     |
| frank vaskelis                  |
| harold george                   |
| henry eimstad                   |
| jay kleiber                     |
| tony roach                      |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| region                |
|:----------------------|
| northwest territories |
| atlantic              |
| prarie                |
| west                  |
| ontario               |
| quebec                |
| yukon                 |
| nunavut               |


label: prarie
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| region_txt                  |
|:----------------------------|
| middle east & north africa  |
| western europe              |
| north america               |
| south america               |
| eastern europe              |
| southeast asia              |
| australasia & oceania       |
| central america & caribbean |
| south asia                  |
| east asia                   |
| sub-saharan africa          |
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:
| continentexp   |
|:---------------|
| america        |
| europe         |
| asia           |
| africa         |
| oceania        |
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:
| value.description                                                                  |
|:-----------------------------------------------------------------------------------|
| ccleaner (remove only)                                                             |
| cmd prompt here powertoy                                                           |
| hp protected your data                                                             |
| microsoft windows media player 10.0.0.3997                                         |
| hewlett packard development company, l.p. hp array configuration utility 7.85.18.0 |
| mcafee, inc. mcafee virusscan enterprise 8.6.0                                     |
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_id   |
|:---------|
| us       |
| ar       |
| au       |
| be       |
| ca       |
| cl       |
| cn       |
| cy       |
| de       |
| es       |
| fr       |
| gb       |
| hu       |
| jp       |
| kr       |
| lt       |
| mx       |
| nl       |
| ru       |
| se       |
| th       |
| bo       |
| co       |
| br       |
| za       |
| ke       |
| ma       |
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:
| geographytype   |
|:----------------|
| city            |
| country/region  |
| state/province  |
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:
| organizer   |
|:------------|
| alex banyan |
| barbara c.  |
| meghan r.   |
| ray k.      |
| louis h.    |
| jack s.     |
| mark brown  |
| john m      |
| nick mason  |
| jack r.     |
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                    |
|:------------------------|
| 2021-01-03 00:00:00.000 |
| 2021-01-04 00:00:00.000 |
| 2021-01-05 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| found detail                                     |
|:-------------------------------------------------|
| computer 3,laptop 1                              |
| computer 1,computer 4,laptop 2,laptop 4,laptop 5 |
| computer 5                                       |
| computer 2,laptop 3                              |
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:
| t_phq9_cat.5      |
|:------------------|
| mild              |
| minimal           |
| severe            |
| moderate          |
| moderately severe |
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:
| team low extras   |
|:------------------|
| west indies       |
| new zealand       |
| pakistan          |
| zimbabwe          |
| india             |
| england           |
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:
| ds_grau_instrucao             |
|:------------------------------|
| ensino médio completo         |
| ensino fundamental incompleto |
| ensino fundamental completo   |
| lê e escreve                  |
| ensino médio incompleto       |
| superior completo             |
| superior incompleto           |
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:
| address1_line1           |
|:-------------------------|
| 5086 nottingham place    |
| 3747 likins avenue       |
| 7691 benedict ct.        |
| 989 caravelle ct         |
| 7230 berrellesa street   |
| 7723 firestone drive     |
| 4948 west th st          |
| 7165 brock lane          |
| 7765 sunsine drive       |
| 931 corte de luna        |
| 7842 ygnacio valley road |


label: 7765 sunsine drive
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:
| executeddate            |
|:------------------------|
| 2021-03-12 00:00:00.000 |
| 2021-03-08 00:00:00.000 |
| 2020-11-19 00:00:00.000 |
| 2020-11-20 00:00:00.000 |
| 2021-01-13 00:00:00.000 |
| 2020-12-08 00:00:00.000 |
| 2020-12-04 00:00:00.000 |
| 2020-12-03 00:00:00.000 |
| 2020-12-02 00:00:00.000 |
| 2020-11-16 00:00:00.000 |
| 2020-11-13 00:00:00.000 |
| 2020-11-12 00:00:00.000 |
| 2020-11-10 00:00:00.000 |
| 2020-11-09 00:00:00.000 |
| 2020-11-06 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:
| sub-category   |
|:---------------|
| binders        |
| paper          |
| storage        |
| art            |
| appliances     |
| labels         |
| envelopes      |
| fasteners      |
| supplies       |
| machines       |
| bookcases      |
| copiers        |
| tables         |
| chairs         |
| accessories    |
| phones         |
| furnishings    |