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:
| host_acceptance_rate   |
|:-----------------------|
| 98%                    |
| 57%                    |
| 75%                    |
| 100%                   |
| 0%                     |
| 79%                    |
| 67%                    |
| 95%                    |
| 80%                    |
| 90%                    |
| 33%                    |
| 49%                    |
| 50%                    |
| 8%                     |
| 20%                    |
| 73%                    |
| 55%                    |
| 94%                    |
| 62%                    |
| 99%                    |
| 85%                    |
| 89%                    |
| 63%                    |
| 83%                    |
| 72%                    |
| 25%                    |
| 88%                    |
| 60%                    |
| 56%                    |
| 97%                    |
| 82%                    |
| 96%                    |
| 23%                    |
| 93%                    |
| 9%                     |
| 86%                    |
| 48%                    |
| 32%                    |
| 31%                    |
| 81%                    |
| 91%                    |
| 66%                    |
| 92%                    |
| 40%                    |
| 84%                    |
| 15%                    |
| 46%                    |
| 17%                    |
| 87%                    |
| 78%                    |
| 21%                    |
| 77%                    |
| 27%                    |
| 71%                    |
| 10%                    |
| 43%                    |
| 5%                     |
| 70%                    |
| 65%                    |
| 41%                    |
| 51%                    |
| 76%                    |
| 35%                    |
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:
| hour of day   |
|:--------------|
| 12 am         |
| 1 am          |
| 2 am          |
| 3 am          |
| 5 am          |
| 4 am          |
| 6 am          |
| 7 am          |
| 3 pm          |
| 2 pm          |
| 1 pm          |
| 12 pm         |
| 10 am         |
| 11 am         |
| 9 am          |
| 8 am          |
| 5 pm          |
| 4 pm          |
| 6 pm          |
| 7 pm          |
| 8 pm          |
| 9 pm          |
| 11 pm         |
| 10 pm         |
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:
| demographictype   |
|:------------------|
| gender            |
| area              |
| income            |
| age group         |
| marital status    |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| country (clusters) 2   |
|:-----------------------|
| cluster1               |
| cluster5               |
| cluster4               |
| cluster3               |
| cluster2               |
| cluster6               |
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:
| dayofweekname   |
|:----------------|
| wednesday       |
| thursday        |
| friday          |
| saturday        |
| monday          |
| tuesday         |
| sunday          |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| fiscal_month_name   |
|:--------------------|
| november            |
| december            |
| 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:
| country   |
|:----------|
| mexico    |
| usa       |
| canada    |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| data pedido             |
|:------------------------|
| 2015-06-21 00:00:00.000 |
| 2017-09-03 00:00:00.000 |
| 2018-09-14 00:00:00.000 |
| 2015-01-16 00:00:00.000 |
| 2017-07-10 00:00:00.000 |
| 2016-12-03 00:00:00.000 |
| 2017-03-13 00:00:00.000 |
| 2015-10-10 00:00:00.000 |
| 2018-02-02 00:00:00.000 |
| 2017-07-16 00:00:00.000 |
| 2016-01-02 00:00:00.000 |
| 2015-05-23 00:00:00.000 |
| 2017-08-30 00:00:00.000 |
| 2017-09-11 00:00:00.000 |
| 2017-04-25 00:00:00.000 |
| 2018-11-14 00:00:00.000 |
| 2016-02-08 00:00:00.000 |
| 2015-04-13 00:00:00.000 |
| 2018-11-13 00:00:00.000 |
| 2016-12-27 00:00:00.000 |
| 2017-08-29 00:00:00.000 |
| 2015-12-26 00:00:00.000 |
| 2015-09-08 00:00:00.000 |
| 2015-12-05 00:00:00.000 |
| 2015-06-09 00:00:00.000 |
| 2016-11-29 00:00:00.000 |
| 2016-12-20 00:00:00.000 |
| 2016-09-26 00:00:00.000 |
| 2016-09-24 00:00:00.000 |
| 2017-07-14 00:00:00.000 |
| 2017-11-27 00:00:00.000 |
| 2018-11-12 00:00:00.000 |
| 2016-08-16 00:00:00.000 |
| 2016-04-02 00:00:00.000 |
| 2015-08-12 00:00:00.000 |
| 2015-04-28 00:00:00.000 |
| 2015-08-08 00:00:00.000 |
| 2015-12-16 00:00:00.000 |
| 2015-11-09 00:00:00.000 |
| 2016-12-25 00:00:00.000 |
| 2016-09-01 00:00:00.000 |
| 2015-09-06 00:00:00.000 |
| 2016-08-29 00:00:00.000 |
| 2016-01-15 00:00:00.000 |
| 2016-10-18 00:00:00.000 |
| 2016-01-27 00:00:00.000 |
| 2016-06-27 00:00:00.000 |
| 2016-08-17 00:00:00.000 |
| 2016-03-14 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| day of week name   |
|:-------------------|
| sunday             |
| monday             |
| tuesday            |
| wednesday          |
| thursday           |
| friday             |
| saturday           |
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:
| driversurname   |
|:----------------|
| rã¤ikkã¶nen     |
| alonso          |
| hamilton        |
| piquet jr.      |
| trulli          |
| kubica          |
| webber          |
| heidfeld        |
| coulthard       |
| schumacher      |
| rosberg         |
| kovalainen      |
| button          |
| de la rosa      |
| barrichello     |
| pablo montoya   |
| glock           |
| nakajima        |
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:
| performance index score grade   |
|:--------------------------------|
| c                               |
| d                               |
| f                               |
| b                               |
| a                               |
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 & year   |
|:-----------------|
| q3 2010          |
| q3 2011          |
| q3 2012          |
| q3 2013          |
| q3 2014          |
| q3 2015          |
| q3 2016          |
| q3 2017          |
| q3 2018          |
| q3 2019          |
| q2 2010          |
| q2 2011          |
| q2 2012          |
| q2 2013          |
| q2 2014          |
| q2 2015          |
| q2 2016          |
| q2 2017          |
| q2 2018          |
| q2 2019          |
| q4 2010          |
| q4 2011          |
| q4 2012          |
| q4 2013          |
| q4 2014          |
| q4 2015          |
| q4 2016          |
| q4 2017          |
| q4 2018          |
| q4 2019          |
| q1 2010          |
| q1 2011          |
| q1 2012          |
| q1 2013          |
| q1 2014          |
| q1 2015          |
| q1 2016          |
| q1 2017          |
| q1 2018          |
| q1 2019          |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| page             |
|:-----------------|
| products         |
| homepage         |
| shipping details |
| contact us       |
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:
| sampling point   |
|:-----------------|
| g-01             |
| g-02             |
| g-03             |
| g-04             |
| g-05             |
| g-06             |
| g-07             |
| g-08             |
| g-09             |
| g-10             |
| g-11             |
| g-12             |
| g-13             |
| g-14             |
| g-15             |
| g-16             |
| g-17             |
| s-1              |
| s-2              |
| s-3              |
| s-4              |
| s-5              |
| s-6              |
| s-6-bis          |
| s-7              |
| s-iw             |
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                    |
|:------------------------|
| 2021-02-01 00:00:00.000 |
| 2021-01-01 00:00:00.000 |
| 2021-03-01 00:00:00.000 |
| 2021-04-01 00:00:00.000 |
| 2021-05-01 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| provincia            |
|:---------------------|
| prince edward island |
| manitoba             |
| british columbia     |
| nova scotia          |
| ontario              |
| newfoundland         |
| quebec               |
| new brunswick        |
| saskachewan          |


label: saskachewan
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:
| calendar startofquarter   |
|:--------------------------|
| 2011-07-01 00:00:00.000   |
| 2013-07-01 00:00:00.000   |
| 2014-07-01 00:00:00.000   |
| 2015-07-01 00:00:00.000   |
| 2017-07-01 00:00:00.000   |
| 2018-07-01 00:00:00.000   |
| 2019-07-01 00:00:00.000   |
| 2021-07-01 00:00:00.000   |
| 2012-07-01 00:00:00.000   |
| 2016-07-01 00:00:00.000   |
| 2020-07-01 00:00:00.000   |
| 2022-07-01 00:00:00.000   |
| 2022-10-01 00:00:00.000   |
| 2011-10-01 00:00:00.000   |
| 2012-10-01 00:00:00.000   |
| 2013-10-01 00:00:00.000   |
| 2014-10-01 00:00:00.000   |
| 2015-10-01 00:00:00.000   |
| 2016-10-01 00:00:00.000   |
| 2017-10-01 00:00:00.000   |
| 2018-10-01 00:00:00.000   |
| 2019-10-01 00:00:00.000   |
| 2020-10-01 00:00:00.000   |
| 2021-10-01 00:00:00.000   |
| 2011-01-01 00:00:00.000   |
| 2013-01-01 00:00:00.000   |
| 2014-01-01 00:00:00.000   |
| 2015-01-01 00:00:00.000   |
| 2017-01-01 00:00:00.000   |
| 2018-01-01 00:00:00.000   |
| 2019-01-01 00:00:00.000   |
| 2021-01-01 00:00:00.000   |
| 2022-01-01 00:00:00.000   |
| 2012-01-01 00:00:00.000   |
| 2016-01-01 00:00:00.000   |
| 2020-01-01 00:00:00.000   |
| 2022-04-01 00:00:00.000   |
| 2012-04-01 00:00:00.000   |
| 2016-04-01 00:00:00.000   |
| 2020-04-01 00:00:00.000   |
| 2011-04-01 00:00:00.000   |
| 2013-04-01 00:00:00.000   |
| 2014-04-01 00:00:00.000   |
| 2015-04-01 00:00:00.000   |
| 2017-04-01 00:00:00.000   |
| 2018-04-01 00:00:00.000   |
| 2019-04-01 00:00:00.000   |
| 2021-04-01 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:
| column1.value.company   |
|:------------------------|
| 1coin                   |
| sjc                     |
| doji                    |
| 1other                  |
| 3banks                  |
| 2group                  |
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                    |
|:------------------------|
| 2018-04-01 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-08-01 00:00:00.000 |
| 2018-06-01 00:00:00.000 |
| 2018-07-01 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:
| annualincome   |
|:---------------|
| $70,000        |
| $40,000        |
| $90,000        |
| $80,000        |
| $60,000        |
| $100,000       |
| $130,000       |
| $150,000       |
| $50,000        |
| $120,000       |
| $160,000       |
| $170,000       |
| $110,000       |
| $30,000        |
| $10,000        |
| $20,000        |
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:
| term        |
|:------------|
| short term  |
| medium term |
| long term   |
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:
| period   |
|:---------|
| 01월     |
| 02월     |
| 03월     |
| 04월     |
| 05월     |
| 06월     |
| 07월     |
| 08월     |
| 09월     |
| 10월     |
| 11월     |
| 12월     |
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:
| shorter name   |
|:---------------|
| shortfin       |
| thresher       |
| whale          |
| great white    |
| wobbegong      |
| sand tiger     |
| blacktip       |
| hammerhead     |
| tiger          |
| basking        |
| sevengill      |
| whitetip       |
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:
| status   |
|:---------|
| approved |
| canceled |
| declined |
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:
| column1._ownerid_value               |
|:-------------------------------------|
| 8a70b0d9-cd99-e511-80d0-a45d36fc0fc4 |
| 82927c9e-505b-eb11-a812-002248047986 |
| 6ce1fa8c-4868-42d2-a00b-d38a281ff351 |
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:
| monthabbr   |
|:------------|
| jul         |
| aug         |
| sep         |
| jan         |
| feb         |
| mar         |
| apr         |
| may         |
| jun         |
| oct         |
| nov         |
| dec         |
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 & year   |
|:-----------------|
| q1 2022          |
| q2 2022          |
| q3 2022          |
| q4 2022          |
| q1 2021          |
| q2 2021          |
| q3 2021          |
| q4 2021          |
| q3 2020          |
| q4 2020          |
| q1 2020          |
| q2 2020          |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| class   |
|:--------|
| l       |
| m       |
| h       |
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_t                  |
|:------------------------|
| 2010-01-01 00:00:00.000 |
| 2010-02-01 00:00:00.000 |
| 2010-03-01 00:00:00.000 |
| 2010-04-01 00:00:00.000 |
| 2010-05-01 00:00:00.000 |
| 2010-06-06 00:00:00.000 |
| 2010-07-01 00:00:00.000 |
| 2010-08-01 00:00:00.000 |
| 2010-09-01 00:00:00.000 |
| 2010-10-01 00:00:00.000 |
| 2010-11-01 00:00:00.000 |
| 2010-12-01 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:
| monthyearnumber   |
|:------------------|
| 20 01             |
| 20 02             |
| 20 03             |
| 20 04             |
| 20 05             |
| 20 06             |
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:
| feature id   |
|:-------------|
| rfid001      |
| rfid002      |
| rfid003      |
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:
| productdetail     |
|:------------------|
| peanut butter     |
| vanilla           |
| cookies           |
| strawberry        |
| caramel ice cream |
| coconut           |
| capuccino         |
| chocolate         |
| banana            |
| natural           |
| black             |
| white             |


label: capuccino
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:
| medico   |
|:---------|
| medico 3 |
| medico 4 |
| medico 5 |
| medico 2 |
| medico 1 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| id_codelist        |
|:-------------------|
| cl_itter107        |
| cl_agri_madre      |
| cl_tipo_dato5      |
| cl_freq            |
| cl_destinazioneuva |
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:
| distribution_channel   |
|:-----------------------|
| ta/to                  |
| direct                 |
| undefined              |
| corporate              |
| gds                    |
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:
| color   |
|:--------|
| e       |
| d       |
| g       |
| f       |
| h       |
| i       |
| j       |
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:
| vat bus_ posting group   |
|:-------------------------|
| national                 |
| export                   |
| eu                       |
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:
| vendedor                         |
|:---------------------------------|
| cartao de recife afogados        |
| ednalva lins de santana          |
| elias fereira da silva           |
| ione ferreira pimentel           |
| thaynan millena muniz da silva   |
| ana paula de lima martins        |
| edilene da silva alves           |
| erica correia de araujo          |
| jose henrique de moura filho     |
| larissa mirelle muniz da silva   |
| lucas santos lima                |
| tatiana quezia araujo de santana |
| jose amilton de almeida neto     |