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:
| fechaultimainterrupcion   |
|:--------------------------|
| 2020-09-21 02:20:41.000   |
| 2020-02-07 16:00:00.000   |
| 2019-10-21 13:55:30.000   |
| 2020-09-11 16:01:57.000   |
| 2020-06-15 15:00:00.000   |
| 2019-12-06 15:32:00.000   |
| 2019-10-24 22:00:00.000   |
| 2020-10-30 02:40:00.000   |
| 2019-08-23 06:30:09.000   |
| 2020-12-05 00:35:12.000   |
| 2020-05-09 14:28:00.000   |
| 2019-08-24 20:43:45.000   |
| 2021-02-19 15:00:00.000   |
| 2020-08-25 21:40:00.000   |
| 2020-11-10 17:30:46.000   |
| 2020-06-05 01:30:39.000   |
| 2019-09-23 18:51:00.000   |
| 2020-03-09 11:00:00.000   |
| 2020-06-25 16:45:00.000   |
| 2019-09-25 20:45:44.000   |
| 2019-07-26 14:23:48.000   |
| 2020-09-21 02:20:00.000   |
| 2021-02-20 14:25:00.000   |
| 2020-01-30 16:00:00.000   |
| 2019-11-05 14:14:00.000   |
| 2020-06-29 00:30:00.000   |
| 2019-09-12 21:09:48.000   |
| 2021-01-09 00:00:00.000   |
| 2019-10-01 16:10:56.000   |
| 2020-04-16 01:15:30.000   |
| 2020-06-05 14:40:12.000   |
| 2019-08-21 19:54:35.000   |
| 2021-03-02 03:10:00.000   |
| 2020-05-21 20:10:00.000   |
| 2021-03-02 09:30:50.000   |
| 2019-07-24 20:50:11.000   |
| 2020-10-09 17:55:00.000   |
| 2019-09-02 15:41:30.000   |
| 2020-11-13 16:40:18.000   |
| 2019-09-26 13:00:00.000   |
| 2020-12-04 04:30:00.000   |
| 2020-04-08 09:30:00.000   |
| 2020-12-04 21:22:00.000   |
| 2020-01-10 16:08:11.000   |
| 2020-12-18 13:45:00.000   |
| 2019-10-08 15:02:40.000   |
| 2019-10-21 15:22:31.000   |
| 2020-04-15 22:10:00.000   |
| 2019-10-17 16:29:02.000   |
| 2020-04-23 16:00:00.000   |
| 2020-11-22 00:34:44.000   |
| 2020-02-28 12:00:00.000   |
| 2021-01-05 13:30:00.000   |
| 2019-10-05 08:00:00.000   |
| 2020-06-16 18:45:00.000   |
| 2019-09-28 08:00:00.000   |
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:
| state                |
|:---------------------|
| california           |
| utah                 |
| washington           |
| michigan             |
| missouri             |
| virginia             |
| georgia              |
| nevada               |
| oklahoma             |
| mississippi          |
| wisconsin            |
| alabama              |
| idaho                |
| kentucky             |
| indiana              |
| arkansas             |
| minnesota            |
| iowa                 |
| louisiana            |
| connecticut          |
| new york             |
| montana              |
| district of columbia |
| massachusetts        |
| delaware             |
| rhode island         |
| maryland             |
| new jersey           |
| south dakota         |
| vermont              |
| south carolina       |
| nebraska             |
| kansas               |
| maine                |
| new hampshire        |
| new mexico           |
| north dakota         |
| west virginia        |
| north carolina       |
| illinois             |
| colorado             |
| florida              |
| texas                |
| tennessee            |
| oregon               |
| arizona              |
| wyoming              |
| pennsylvania         |
| 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:
| fiscal year          |
|:---------------------|
| fy definition needed |
| fy10                 |
| fy11                 |
| fy12                 |
| fy13                 |
| fy14                 |
| fy15                 |
| fy16                 |
| fy17                 |
| fy18                 |
| fy19                 |
| fy20                 |
| fy21                 |
| fy22                 |
| fy23                 |
| fy24                 |
| fy25                 |
| fy26                 |
| fy27                 |
| fy28                 |
| fy29                 |
| fy30                 |
| fy02                 |
| fy03                 |
| fy04                 |
| fy05                 |
| fy06                 |
| fy07                 |
| fy08                 |
| fy09                 |


label: fy definition needed
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:
| accountid                            |
|:-------------------------------------|
| e21ef153-577e-4132-94b0-3d0108d97def |
| ddf7049b-4fc5-43b3-8888-54f5690b52da |
| 2ec3335a-b20b-43b3-b6dc-40a5846936e2 |
| 93881bb4-94a1-4204-8fae-467bbad5402c |
| c46440f5-8969-4bc5-b1a3-49623a28de29 |
| 48845453-b8e0-441a-8e28-50936f8e3ef0 |
| b02ea61a-fb02-41a6-a6bf-a615d570f862 |
| 65aba7e4-58e1-4294-ba5b-07ebc95c87f6 |
| 9c157f75-941a-41d5-9d7b-6172e3e625d8 |
| 506db822-2ab4-4c1a-9f08-abb55a5fb9cb |
| 760e5d35-37e1-4bb8-b293-8bfa2cd5085f |
| 57f0a6c2-9f0d-41d3-ba4e-f02f97d40a58 |
| 4367d7c8-1af3-47da-950d-8a377b6f8d8e |
| bc7c187b-05c9-42d9-b490-f3af99cba4e6 |
| 8ee64f7d-aa4a-4786-990b-174f6ed0eda0 |
| ae5af670-5289-48d8-9415-19557e9bf437 |
| 5a855b6a-1fd4-4fb5-844b-431e6dc9e58c |
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   |
|:---------------|
| labels         |
| tables         |
| storage        |
| furnishings    |
| art            |
| phones         |
| binders        |
| appliances     |
| paper          |
| chairs         |
| accessories    |
| bookcases      |
| envelopes      |
| fasteners      |
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_category   |
|:-------------------|
| tech               |
| christmas          |
| knitting           |
| kitchen            |
| painting           |
| craft              |
| mystery kit        |
| science            |
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:
| categorización              |
|:----------------------------|
| sin asignación              |
| control de accesos          |
| gestión de vulnerabilidades |
| documentación               |
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:
| surveyconfigurations10.column1.grouping (groups)                                                                            |
|:----------------------------------------------------------------------------------------------------------------------------|
| teachers are rewarded or recognized for improving our teaching practices                                                    |
| teacher appraisals emphasize new teaching practices                                                                         |
| the school supports discussions among teachers about new teaching practices                                                 |
| teachers use peer review to improve instruction (including review of lesson plans and observation of classroom instruction) |
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:
| timeframe               |
|:------------------------|
| 2020-12-01 00:00:00.000 |
| 2020-11-01 00:00:00.000 |
| 2020-10-01 00:00:00.000 |
| 2020-09-01 00:00:00.000 |
| 2020-08-01 00:00:00.000 |
| 2020-07-01 00:00:00.000 |
| 2020-06-01 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2020-04-01 00:00:00.000 |
| 2020-03-01 00:00:00.000 |
| 2020-02-01 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| product category   |
|:-------------------|
| clothes            |
| stationery         |
| toys               |
| sport              |
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   |
|:----------|
| paseo     |
| velo      |
| vtt       |
| carretera |
| amarilla  |
| montana   |
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:
| 所属行业              |
|:----------------------|
| 移动互联网,电商       |
| 金融                  |
| 移动互联网            |
| 教育                  |
| 电商,消费生活         |
| 数据服务,移动互联网   |
| 消费生活              |
| 电商                  |
| 金融,数据服务         |
| 移动互联网,企业服务   |
| 移动互联网,金融       |
| 文娱丨内容            |
| 旅游                  |
| 移动互联网,社交       |
| 移动互联网,教育       |
| 数据服务,广告营销     |
| 汽车丨出行            |
| 移动互联网,数据服务   |
| 企业服务              |
| 移动互联网,文娱丨内容 |
| 移动互联网,游戏       |
| 数据服务,企业服务     |
| 人工智能              |
| 企业服务,数据服务     |
| 数据服务              |
| 游戏                  |
| 移动互联网,消费生活   |
| 旅游,消费生活         |
| 数据服务,金融         |
| 电商,移动互联网       |
| 医疗丨健康            |
| 物流丨运输            |
| 金融,企业服务         |
| 电商,企业服务         |
| 移动互联网,医疗丨健康 |
| 电商,旅游             |
| 数据服务,文娱丨内容   |
| 游戏,社交             |
| 数据服务,硬件         |
| 数据服务,医疗丨健康   |
| 硬件,数据服务         |
| 企业服务,区块链       |
| 广告营销,文娱丨内容   |
| 广告营销              |
| 消费生活,教育         |
| 硬件                  |
| 其他                  |
| 数据服务,人工智能     |
| 工具                  |
| 移动互联网,硬件       |
| 信息安全,数据服务     |
| 电商,其他             |
| 教育 移动互联网       |
| 医疗丨健康,移动互联网 |
| 文娱丨内容,广告营销   |
| 电商,数据服务         |
| 文娱丨内容,移动互联网 |
| 区块链,电商           |
| 社交                  |
| 游戏,移动互联网       |
| 移动互联网,其他       |
| 移动互联网,广告营销   |
| 电商,医疗丨健康       |
| 教育,社交             |
| 医疗健康              |
| 其他,企业服务         |
| 企业服务,移动互联网   |
| 电商,金融             |
| 社交,消费生活         |
| 企业服务,硬件         |
| 消费生活,其他         |
| 教育,数据服务         |
| 电商,广告营销         |
| 金融,不限             |
| 游戏,广告营销         |
| 信息安全              |
| 医疗丨健康,数据服务   |
| 医疗丨健康,企业服务   |
| 软件开发              |
| 金融,人工智能         |
| 电商,硬件             |
| 通讯电子              |
| 企业服务,金融         |
| 教育,移动互联网       |
| 电商,文娱丨内容       |
| 不限                  |
| 金融,移动互联网       |
| 金融,电商             |
| 移动互联网,信息安全   |
| 游戏,文娱丨内容       |
| 房产家居              |
| 硬件,人工智能         |
| 旅游,文娱丨内容       |
| 数据服务,信息安全     |
| 教育,文娱丨内容       |
| 硬件,其他             |
| 移动互联网,人工智能   |
| 消费生活,移动互联网   |
| 移动互联网,区块链     |
| 消费生活,金融         |
| 社交,金融             |
| 电子商务              |
| 企业服务,其他         |
| 游戏,医疗丨健康       |
| 企业服务,文娱丨内容   |
| 消费生活,电商         |
| 移动互联网,旅游       |
| 广告营销,移动互联网   |
| 金融,医疗丨健康       |
| 企业服务,电商         |
| 其他,消费生活         |
| 社交,游戏             |
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 quarter   |
|:-----------------|
| q4               |
| q1               |
| q2               |
| q3               |
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:
| continent     |
|:--------------|
| africa        |
| europe        |
| north america |
| asia          |
| south america |
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:
| sla__c   |
|:---------|
| gold     |
| silver   |
| platinum |
| bronze   |
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   |
|:----------|
| l         |
| n         |
| s         |
| m         |
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                    |
|:------------------------|
| 2019-01-01 12:05:00.000 |
| 2019-01-01 12:06:00.000 |
| 2019-01-01 13:12:00.000 |
| 2019-01-01 15:37:00.000 |
| 2019-01-09 14:06:00.000 |
| 2019-01-10 01:41:00.000 |
| 2019-01-10 01:47:00.000 |
| 2019-01-10 06:27:00.000 |
| 2019-01-12 13:31:00.000 |
| 2019-01-19 17:19:00.000 |
| 2019-01-19 17:22:00.000 |
| 2019-01-19 18:29:00.000 |
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:
| flavour child          |
|:-----------------------|
| malt forward           |
| dark & dry             |
| fruity & vinous        |
| earthy                 |
| soft & silky/malty     |
| bright & yeasty        |
| dark                   |
| fruit & toffee/caramel |
| clean/delicate fruit   |
| earthy & dry           |
| toasty & nutty         |
| bold/strong hops       |
| malt-accented          |
| brisk hop              |
| spicy & meaty          |
| subdued smolder        |
| delicate               |
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:
| labtyp   |
|:---------|
| t        |
| z        |
| o        |
| s        |
| l        |
| x1       |
| a        |
| b21      |
| b13      |
| m        |
| n        |
| q        |
| y        |
| v        |
| d        |
| i3       |
| i        |
| i2       |
| g        |
| h        |
| h2       |
| i5       |
| i14      |
| i16      |
| a2       |
| a3       |
| b14      |
| b22      |
| b18      |
| b23      |
| b17      |
| b15      |
| b11      |
| b25      |
| b26      |
| b27      |
| b29      |
| b30      |
| b42      |
| b44      |
| b1       |
| b41      |
| b43      |
| a1       |
| a4       |
| x2       |
| x        |
| b33      |
| b35      |
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:
| action name                   |
|:------------------------------|
| 3 - useraction_crosshighlight |
| 4 - useraction_crosshighlight |
| 5 - useraction_crosshighlight |
| 6 - useraction_changepage     |
| 7 - useraction_crosshighlight |
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:
| target                      |
|:----------------------------|
| account history             |
| account summary             |
| funds transfer              |
| faq                         |
| view deposit details        |
| online statement enrollment |
| profile update              |
| bill manager form           |
| customer support            |
| bill manager enrollment     |
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:
| shortname   |
|:------------|
| andr        |
| nick        |
| matt        |
| gran        |
| scot        |
| kand        |
| ishw        |
| sean        |
| bria        |
| kath        |
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:
| currency code   |
|:----------------|
| sek             |
| lvl             |
| eur             |
| ltl             |
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:
| endofmonth date         |
|:------------------------|
| 2018-01-31 00:00:00.000 |
| 2018-02-28 00:00:00.000 |
| 2018-03-31 00:00:00.000 |
| 2018-04-30 00:00:00.000 |
| 2018-05-31 00:00:00.000 |
| 2018-06-30 00:00:00.000 |
| 2018-07-31 00:00:00.000 |
| 2018-08-31 00:00:00.000 |
| 2018-09-30 00:00:00.000 |
| 2018-10-31 00:00:00.000 |
| 2018-11-30 00:00:00.000 |
| 2018-12-31 00:00:00.000 |
| 2019-01-31 00:00:00.000 |
| 2019-02-28 00:00:00.000 |
| 2019-03-31 00:00:00.000 |
| 2019-04-30 00:00:00.000 |
| 2019-05-31 00:00:00.000 |
| 2019-06-30 00:00:00.000 |
| 2019-07-31 00:00:00.000 |
| 2019-08-31 00:00:00.000 |
| 2019-09-30 00:00:00.000 |
| 2019-10-31 00:00:00.000 |
| 2019-11-30 00:00:00.000 |
| 2019-12-31 00:00:00.000 |
| 2020-01-31 00:00:00.000 |
| 2020-02-29 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
| 2020-04-30 00:00:00.000 |
| 2020-05-31 00:00:00.000 |
| 2020-06-30 00:00:00.000 |
| 2020-07-31 00:00:00.000 |
| 2020-08-31 00:00:00.000 |
| 2020-09-30 00:00:00.000 |
| 2020-10-31 00:00:00.000 |
| 2020-11-30 00:00:00.000 |
| 2020-12-31 00:00:00.000 |
| 2021-01-31 00:00:00.000 |
| 2021-02-28 00:00:00.000 |
| 2021-03-31 00:00:00.000 |
| 2021-04-30 00:00:00.000 |
| 2021-05-31 00:00:00.000 |
| 2021-06-30 00:00:00.000 |
| 2021-07-31 00:00:00.000 |
| 2021-08-31 00:00:00.000 |
| 2021-09-30 00:00:00.000 |
| 2021-10-31 00:00:00.000 |
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:
| quarterincalendar   |
|:--------------------|
| q1 2014             |
| q2 2014             |
| q3 2014             |
| q4 2014             |
| q1 2015             |
| q2 2015             |
| q3 2015             |
| q4 2015             |
| q1 2016             |
| q2 2016             |
| q3 2016             |
| q4 2016             |
| q1 2017             |
| q2 2017             |
| q3 2017             |
| q4 2017             |
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:
| colors   |
|:---------|
| blue     |
| black    |
| red      |
| yellow   |
| silver   |
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:
| vision_labels.9               |
|:------------------------------|
| phenomenon                    |
| night                         |
| computer wallpaper            |
| monochrome                    |
| eclipse                       |
| celestial event               |
| astronomical object           |
| monochrome photography        |
| darkness                      |
| moon                          |
| crescent                      |
| event                         |
| midnight                      |
| light                         |
| black and white               |
| atmosphere of earth           |
| daytime                       |
| product design                |
| font                          |
| corona                        |
| sunlight                      |
| circle                        |
| lunar eclipse                 |
| moonlight                     |
| garden                        |
| space                         |
| macro photography             |
| sky                           |
| geology                       |
| mountain range                |
| lighting                      |
| tree                          |
| geological phenomenon         |
| mountain                      |
| lens flare                    |
| furniture                     |
| lens                          |
| outdoor play equipment        |
| cloud                         |
| road trip                     |
| sunglasses                    |
| race                          |
| adventurer                    |
| recreational vehicle          |
| yard                          |
| park                          |
| personal protective equipment |
| grass                         |
| automotive exterior           |
| ecosystem                     |
| spoke                         |
| mode of transport             |
| plant                         |
| sphere                        |
| shore                         |
| cycling                       |
| full moon                     |
| sun                           |
| outer space                   |
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:
| matriz         |
|:---------------|
| são paulo      |
| belo horizonte |
| curitiba       |
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:
| salary group   |
|:---------------|
| 0-40k          |
| 40-60k         |
| 60-80k         |
| 80-100k        |
| more of 100k   |
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:
| link.1                                                                             |
|:-----------------------------------------------------------------------------------|
| http://www.seloger.com/annonces/achat/appartement/malakoff-92/118330861.htm        |
| http://www.seloger.com/annonces/achat/appartement/malakoff-92/118330941.htm        |
| http://www.seloger.com/annonces/achat/bureaux/malakoff-92/118368993.htm            |
| http://www.seloger.com/annonces/achat/appartement/malakoff-92/118379623.htm        |
| http://www.seloger.com/annonces/achat-de-prestige/maison/malakoff-92/118381705.htm |
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:
| deport   |
|:---------|
| a        |
| b        |
| c        |
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:
| when are you most productive?   |
|:--------------------------------|
| mid-morning                     |
| early morning                   |
| late night                      |
| afternoon                       |
| evening                         |
| lunchtime                       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| value.u_level_of_performance_met   |
|:-----------------------------------|
| nomeasurement                      |
| nodata                             |
| defaulted                          |
| metexpected                        |
| metminimum                         |