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:
| project_name   |
|:---------------|
| project3       |
| project4       |
| project5       |
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:
| f14                 |
|:--------------------|
| sh2_atn_emg(5)      |
| wr21#lg_atn_dol(8)  |
| wr23#lg_pos_spd(20) |
| wl3_atn_rtt(8)      |
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   |
|:-----|
| fy17 |
| fy18 |
| fy19 |
| fy20 |
| fy16 |
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:
| 年份   |
|:-------|
| 108年  |
| 107年  |
| 106年  |
| 105年  |
| 104年  |
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   |
|:----------|
| p3        |
| p4        |
| p5        |
| p6        |
| p7        |
| p8        |
| p9        |
| p10       |
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:
| rate month   |
|:-------------|
| august       |
| december     |
| october      |
| september    |
| november     |
| april        |
| march        |
| july         |
| may          |
| june         |
| february     |
| january      |
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   |
|:--------|
| brand1  |
| brand2  |
| brand3  |
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:
| loser_hand   |
|:-------------|
| u            |
| r            |
| l            |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| internalemailaddress                    |
|:----------------------------------------|
| partner@qwaredemos.onmicrosoft.com      |
| noreply@paribuscloud.com                |
| scapp_sa@elad.co.il                     |
| crmoln2@microsoft.com                   |
| crmoln@microsoft.com                    |
| service.user@qwaredemos.onmicrosoft.com |
| john.smith@qwaredemos.onmicrosoft.com   |
| tom.peters@qwaredemos.onmicrosoft.com   |
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:
| content.public_id   |
|:--------------------|
| eb-c0118            |
| eb-c0119            |
| eb-c0124            |
| eb-c0135            |
| eb-b5505            |
| eb-b5506            |
| eb-b5514            |
| eb-b5515            |
| eb-b5516            |
| eb-b5517            |
| eb-b5519            |
| eb-b5522            |
| eb-b5528            |
| eb-b5531            |
| eb-b5535            |
| eb-c0147            |
| eb-c0152            |
| eb-b5486            |
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:
| fmr   |
|:------|
| f     |
| m     |
| 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:
| firstname       |
|:----------------|
| dominik         |
| martin          |
| jonas           |
| ali             |
| denis           |
| margarete       |
| julian          |
| christian       |
| richard         |
| sebastian       |
| pascal          |
| oliver          |
| daniel          |
| interner        |
| andreas         |
| enis            |
| tanja           |
| vaddi           |
| fatih           |
| partner         |
| robin           |
| gangadhar reddy |
| patrick         |
| simon           |
| eckhard         |
| monika          |
| mathias         |
| nicu test       |
| nicu            |
| philipp         |
| manuel          |
| david           |
| lucas           |
| madeleine       |
| maximilian      |
| microsoft       |
| seyma           |
| nicholas        |
| sudhir          |
| lavinia         |
| anton           |
| lena            |
| prasad          |
| salah           |
| labeeb          |
| simone          |
| mergim          |
| sassan          |
| nadeem          |
| prof. dr. peter |
| max             |
| ertan           |
| dr. jaroslav    |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| model number   |
|:---------------|
| x1 yoga        |
| p1             |
| x1 carbon      |
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:
| dates                   |
|:------------------------|
| 2020-02-03 00:00:00.000 |
| 2020-05-20 00:00:00.000 |
| 2020-05-05 00:00:00.000 |
| 2020-04-06 00:00:00.000 |
| 2020-04-18 00:00:00.000 |
| 2020-05-25 00:00:00.000 |
| 2020-04-27 00:00:00.000 |
| 2020-04-01 00:00:00.000 |
| 2020-04-12 00:00:00.000 |
| 2020-03-30 00:00:00.000 |
| 2020-03-25 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:
| [approval note] baseline spend currency code   |
|:-----------------------------------------------|
| inr                                            |
| usd                                            |
| gbp                                            |
| sgd                                            |
| cad                                            |
| aed                                            |
| hkd                                            |
| eur                                            |
| jpy                                            |
| myr                                            |
| aud                                            |
| rub                                            |
| zar                                            |
| brl                                            |
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                    |
|:------------------------|
| 2018-01-02 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2018-02-01 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:
| sub-category   |
|:---------------|
| paper          |
| storage        |
| art            |
| binders        |
| appliances     |
| labels         |
| envelopes      |
| fasteners      |
| supplies       |
| bookcases      |
| machines       |
| copiers        |
| tables         |
| chairs         |
| phones         |
| accessories    |
| furnishings    |
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:
| warehouse state   |
|:------------------|
| nevada            |
| indiana           |
| new york          |
| kansas            |
| florida           |
| california        |
| minnesota         |
| texas             |
| louisiana         |
| ohio              |
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:
| featureservicestatus   |
|:-----------------------|
| serviceoperational     |
| servicerestored        |
| investigating          |
| restoringservice       |
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:
| end date                |
|:------------------------|
| 2018-05-06 00:00:00.000 |
| 2018-07-07 00:00:00.000 |
| 2018-09-09 00:00:00.000 |
| 2018-11-11 00:00:00.000 |
| 2018-12-01 00:00:00.000 |
| 2018-08-31 00:00:00.000 |
| 2018-05-05 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:
| exit_date               |
|:------------------------|
| 2020-09-12 12:09:00.000 |
| 2020-09-12 12:57:00.000 |
| 2020-09-14 13:47:00.000 |
| 2020-09-13 17:54:00.000 |
| 2020-09-13 13:53:00.000 |
| 2020-09-13 11:27:00.000 |
| 2020-09-12 10:39:00.000 |
| 2020-09-13 20:14:00.000 |
| 2020-09-13 18:44:00.000 |
| 2020-09-12 14:26:00.000 |
| 2020-09-13 10:25:00.000 |
| 2020-09-12 05:40:00.000 |
| 2020-09-12 10:03:00.000 |
| 2020-09-12 13:03:00.000 |
| 2020-09-13 16:29:00.000 |
| 2020-09-13 18:14:00.000 |
| 2020-09-13 16:51:00.000 |
| 2020-09-12 17:31:00.000 |
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:
| county hubs chir region                |
|:---------------------------------------|
| benzie                                 |
| leelanau                               |
| grand traverse                         |
| all district health department #10 hub |
| crawford                               |
| kalkaska                               |
| lake                                   |
| manistee                               |
| mason                                  |
| mecosta                                |
| missaukee                              |
| newaygo                                |
| oceana                                 |
| wexford                                |
| northwest michigan hub                 |
| antrim                                 |
| charlevoix                             |
| emmet                                  |
| otsego                                 |
| district health department #4 hub      |
| alpena                                 |
| cheboygan                              |
| montmorency                            |
| presque isle                           |
| all hubs                               |
| all gt hub                             |
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:
| fulldate                |
|:------------------------|
| 2019-11-01 00:00:00.000 |
| 2019-11-02 00:00:00.000 |
| 2019-11-04 00:00:00.000 |
| 2019-11-05 00:00:00.000 |
| 2019-11-06 00:00:00.000 |
| 2019-11-07 00:00:00.000 |
| 2019-11-08 00:00:00.000 |
| 2019-11-11 00:00:00.000 |
| 2019-11-12 00:00:00.000 |
| 2019-11-13 00:00:00.000 |
| 2019-11-14 00:00:00.000 |
| 2019-11-15 00:00:00.000 |
| 2019-11-17 00:00:00.000 |
| 2019-11-18 00:00:00.000 |
| 2019-11-19 00:00:00.000 |
| 2019-11-20 00:00:00.000 |
| 2019-11-21 00:00:00.000 |
| 2019-11-22 00:00:00.000 |
| 2019-11-25 00:00:00.000 |
| 2019-11-26 00:00:00.000 |
| 2019-11-27 00:00:00.000 |
| 2019-11-28 00:00:00.000 |
| 2019-11-29 00:00:00.000 |
| 2019-11-30 00:00:00.000 |
| 2020-11-02 00:00:00.000 |
| 2020-11-03 00:00:00.000 |
| 2020-11-04 00:00:00.000 |
| 2020-11-05 00:00:00.000 |
| 2020-11-06 00:00:00.000 |
| 2020-11-09 00:00:00.000 |
| 2020-11-10 00:00:00.000 |
| 2020-11-11 00:00:00.000 |
| 2020-11-16 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:
| shopkey   |
|:----------|
| x10048    |
| x10065    |
| x10052    |
| x10008    |
| x10050    |
| x10025    |
| x10076    |
| x10004    |
| x10011    |
| x10057    |
| x10009    |
| x10045    |
| x10026    |
| x10020    |
| x10018    |
| x10047    |
| x10075    |
| x10086    |
| x10056    |
| x10046    |
| x10027    |
| x10081    |
| x10058    |
| x10003    |
| x10005    |
| x10029    |
| x10068    |
| x10051    |
| x10028    |
| x10053    |
| x10073    |
| x10035    |
| x10067    |
| x10022    |
| x10016    |
| x10014    |
| x10039    |
| x10023    |
| x10063    |
| x10036    |
| x10078    |
| x10077    |
| x10079    |
| x10062    |
| x10069    |
| x10032    |
| x10043    |
| x10082    |
| x10085    |
| x10019    |
| x10034    |
| x10033    |
| x10044    |
| x10007    |
| x10041    |
| x10030    |
| x10038    |
| x10070    |
| x10061    |
| x10059    |
| x10015    |
| x10080    |
| x10064    |
| x10060    |
| x10006    |
| x10055    |
| x10021    |
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:
| quarter   |
|:----------|
| 12q1      |
| 12q2      |
| 12q3      |
| 12q4      |
| 13q1      |
| 13q2      |
| 13q3      |
| 13q4      |
| 14q1      |
| 14q2      |
| 14q3      |
| 14q4      |
| 15q1      |
| 15q2      |
| 15q3      |
| 15q4      |
| 16q1      |
| 16q2      |
| 16q3      |
| 16q4      |
| 17q1      |
| 17q2      |
| 17q3      |
| 17q4      |
| 18q1      |
| 18q2      |
| 18q3      |
| 18q4      |
| 19q1      |
| 19q2      |
| 19q3      |
| 19q4      |
| 20q1      |
| 20q2      |
| 20q3      |
| 20q4      |
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 sub-category           |
|:-------------------------------|
| computer peripherals           |
| appliances                     |
| storage & organization         |
| binders and binder accessories |
| envelopes                      |
| paper                          |
| office furnishings             |
| labels                         |
| office machines                |
| telephones and communication   |
| pens & art supplies            |
| scissors, rulers and trimmers  |
| tables                         |
| copiers and fax                |
| rubber bands                   |
| chairs & chairmats             |
| bookcases                      |
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:
| pay per date            |
|:------------------------|
| 2019-03-03 00:00:00.000 |
| 2019-03-10 00:00:00.000 |
| 2019-03-17 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:
| text for translation   |
|:-----------------------|
| नमस्ते दुनिया               |
| bonjour le monde       |
| ハローワールド         |
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:
| backername           |
|:---------------------|
| unknown              |
| ag barr              |
| booker group         |
| boots group          |
| bp plc               |
| cairn energy plc     |
| melrose resources    |
| glaxosmithkline      |
| land securities gp   |
| unilever plc         |
| oceonics             |
| rio tinto            |
| harry ramsden        |
| spirax-sarco engr    |
| novae group          |
| skynet telemtcs inc  |
| bristow group        |
| rolls-royce hldgs    |
| reed elsevier        |
| telenor asa          |
| mti global inc       |
| conocophillips       |
| starwood ppty tr     |
| choice care group    |
| park holidays        |
| gala bingo           |
| bioagilytix          |
| india capital growth |
| k-reit asia          |
| std life aberdeen    |
| saigon tel           |
| kingdom group        |
| londonmetric prop    |
| hagl                 |
| polar capital funds  |
| phoenix group hold   |
| amcor limited        |
| terrace hill group   |
| atlas copco          |
| newton asian fund    |
| eddington cap funds  |
| ocean dial gw india  |
| longleaf mgmt funds  |
| macq asia new stars  |
| an phu irradiation   |
| caspian              |
| easybox              |
| dewan housing fin    |
| ocean dial group     |
| intercorr            |
| retif                |
| perlus microcap      |
| pragma funds         |
| kerzner intl ltd     |
| tge marine ag        |
| latshaw group        |
| ntasian funds        |
| mspea funds          |
| liberation group     |
| seven inv mgmt       |
| edinmore group       |
| ftse options         |
| nova caledonia       |
| amber group          |
| incisive media       |
| ermitage             |
| general practice     |
| cazenove group       |
| edinmore invmts      |
| icap                 |
| nexxtdrive           |
| nova springboard     |
| redleaf ppty funds   |
| sloane club          |
| garlandheath         |
| bowers & wilkins     |
| sterling industries  |
| cooke optics         |
| deep sea electronics |
| cash etc             |
| arlington capital    |
| fx hedge             |
| cobehold             |
| caledonia nl         |
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:
| element       |
|:--------------|
| lithium       |
| beryllium     |
| boron         |
| carbon        |
| nitrogen      |
| oxygen        |
| fluorine      |
| neon          |
| sodium        |
| magnesium     |
| aluminium     |
| silicon       |
| phosphorus    |
| sulfur        |
| chlorine      |
| argon         |
| potassium     |
| calcium       |
| scandium      |
| titanium      |
| vanadium      |
| chromium      |
| manganese     |
| iron          |
| cobalt        |
| nickel        |
| copper        |
| zinc          |
| gallium       |
| germanium     |
| arsenic       |
| selenium      |
| bromine       |
| krypton       |
| rubidium      |
| strontium     |
| yttrium       |
| zirconium     |
| niobium       |
| molybdenum    |
| technetium    |
| ruthenium     |
| rhodium       |
| palladium     |
| silver        |
| cadmium       |
| indium        |
| tin           |
| antimony      |
| tellurium     |
| iodine        |
| xenon         |
| caesium       |
| barium        |
| lanthanum     |
| cerium        |
| praseodymium  |
| neodymium     |
| promethium    |
| samarium      |
| europium      |
| gadolinium    |
| terbium       |
| dysprosium    |
| holmium       |
| erbium        |
| thulium       |
| ytterbium     |
| lutetium      |
| hafnium       |
| tantalum      |
| tungsten      |
| rhenium       |
| osmium        |
| iridium       |
| platinum      |
| gold          |
| mercury       |
| thallium      |
| lead          |
| bismuth       |
| polonium      |
| astatine      |
| radon         |
| francium      |
| radium        |
| actinium      |
| thorium       |
| protactinium  |
| uranium       |
| neptunium     |
| plutonium     |
| americium     |
| curium        |
| berkelium     |
| californium   |
| einsteinium   |
| fermium       |
| mendelevium   |
| nobelium      |
| lawrencium    |
| rutherfordium |
| dubnium       |
| seaborgium    |
| bohrium       |
| hassium       |
| meitnerium    |
| darmstadtium  |
| roentgenium   |
| copernicium   |
| nihonium      |
| flerovium     |
| moscovium     |
| livermorium   |
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 name                                  |
|:----------------------------------------------|
| cardinal binder, clear                        |
| sanford canvas, water color                   |
| globeweis mailers, with clear poly window     |
| konica card printer, red                      |
| enermax router, erganomic                     |
| wilson jones hole reinforcements, durable     |
| hon steel folding chair, black                |
| kraft mailers, security-tint                  |
| advantus clamps, metal                        |
| jiffy peel and seal, with clear poly window   |
| globeweis interoffice envelope, security-tint |
| jiffy clasp envelope, recycled                |
| enermax mouse, usb                            |
| acme box cutter, serrated                     |
| deflect-o clock, black                        |


label: enermax router, erganomic
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                     |
|:-------------------------|
| fc bayern münchen        |
| club brugge kv           |
| borussia mönchengladbach |
| fk dynamo kyiv           |
| chelsea fc               |
| juventus fc              |
| liverpool fc             |
| manchester city fc       |
| sevilla fc               |
| manchester united fc     |
| stade rennais fc 1901    |
| club atlético de madrid  |
| fk krasnodar             |
| fc barcelona             |
| ss lazio                 |
| real madrid cf           |
| atalanta bc              |
| fc internazionale milano |
| ferencvárosi tc          |
| paris saint-germain fc   |
| fc porto                 |
| rb leipzig               |
| olympique de marseille   |
| medipol başakşehir fk    |
| fc red bull salzburg     |
| fk lokomotiv moskva      |
| fk shakhtar donetsk      |
| pae olympiakos sfp       |
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           |
|:---------------|
| arleta byrns   |
| jed rice       |
| dodie hilty    |
| toni oliver    |
| freya turovsky |
| dana tucker    |
| james hessel   |
| addy hilty     |
| cleta oliver   |
| manny walsh    |
| john arnold    |
| caryl smith    |
| jack hessel    |
| rachel rice    |
| john william   |
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:
| type   |
|:-------|
| e      |
| v      |
| x      |
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                    |
|:------------------------|
| 2018-04-16 00:00:00.000 |
| 2018-04-17 00:00:00.000 |
| 2019-04-13 00:00:00.000 |
| 2019-04-14 00:00:00.000 |