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:
| date                    |
|:------------------------|
| 2021-06-11 00:00:00.000 |
| 2021-06-02 00:00:00.000 |
| 2021-06-03 00:00:00.000 |
| 2021-06-01 00:00:00.000 |
| 2021-06-10 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:
| edit][host           |
|:---------------------|
| italy                |
| belgium              |
| yugoslavia           |
| france               |
| west germany         |
| sweden               |
| england              |
| belgium netherlands  |
| portugal             |
| austria  switzerland |
| poland ukraine       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| clarity   |
|:----------|
| vvs1      |
| vvs2      |
| vs1       |
| vs2       |
| si1       |
| si2       |
| si3       |
| i1        |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| nom du mois   |
|:--------------|
| january 10    |
| february 10   |
| march 10      |
| april 10      |
| may 10        |
| june 10       |
| july 10       |
| august 10     |
| september 10  |
| october 10    |
| november 10   |
| december 10   |
| january 11    |
| february 11   |
| march 11      |
| april 11      |
| may 11        |
| june 11       |
| july 11       |
| august 11     |
| september 11  |
| october 11    |
| november 11   |
| december 11   |
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:
| category    |
|:------------|
| components  |
| accessories |
| clothing    |
| bikes       |
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:
| global_area_dsc   |
|:------------------|
| iramea            |
| europe            |
| north america     |
| latam             |
| aspac             |
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:
| hiringdate              |
|:------------------------|
| 2018-02-18 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-08-14 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2018-05-03 00:00:00.000 |
| 2017-10-17 00:00:00.000 |
| 2018-10-17 00:00:00.000 |
| 2018-01-02 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:
| value      |
|:-----------|
| $21        |
| $2         |
| $567       |
| $200,000   |
| $54,541    |
| $5         |
| $454       |
| $54        |
| $45,545    |
| $2,151,451 |
| $51,516    |
| $51,123    |
| $11,252    |
| $400,000   |
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:
| endofmonth              |
|:------------------------|
| 2021-02-28 00:00:00.000 |
| 2021-01-31 00:00:00.000 |
| 2020-12-31 00:00:00.000 |
| 2020-11-30 00:00:00.000 |
| 2020-10-31 00:00:00.000 |
| 2020-09-30 00:00:00.000 |
| 2020-08-31 00:00:00.000 |
| 2020-07-31 00:00:00.000 |
| 2020-06-30 00:00:00.000 |
| 2020-05-31 00:00:00.000 |
| 2020-04-30 00:00:00.000 |
| 2020-03-31 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| date                    |
|:------------------------|
| 2021-09-15 00:00:00.000 |
| 2021-09-16 00:00:00.000 |
| 2021-09-14 00:00:00.000 |
| 2021-09-19 00:00:00.000 |
| 2021-09-20 00:00:00.000 |
| 2021-09-17 00:00:00.000 |
| 2021-09-18 00:00:00.000 |
| 2021-10-06 00:00:00.000 |
| 2021-09-22 00:00:00.000 |
| 2021-10-07 00:00:00.000 |
| 2021-10-08 00:00:00.000 |
| 2021-09-25 00:00:00.000 |
| 2021-10-11 00:00:00.000 |
| 2021-10-10 00:00:00.000 |
| 2021-09-21 00:00:00.000 |
| 2021-09-29 00:00:00.000 |
| 2021-10-30 00:00:00.000 |
| 2021-11-02 00:00:00.000 |
| 2021-11-04 00:00:00.000 |
| 2021-11-12 00:00:00.000 |
| 2021-10-25 00:00:00.000 |
| 2021-11-05 00:00:00.000 |
| 2021-11-07 00:00:00.000 |
| 2021-11-06 00:00:00.000 |
| 2021-11-11 00:00:00.000 |
| 2021-11-14 00:00:00.000 |
| 2021-10-17 00:00:00.000 |
| 2021-10-19 00:00:00.000 |
| 2021-11-19 00:00:00.000 |
| 2021-10-18 00:00:00.000 |
| 2021-11-10 00:00:00.000 |
| 2021-10-26 00:00:00.000 |
| 2021-10-28 00:00:00.000 |
| 2021-09-24 00:00:00.000 |
| 2021-10-03 00:00:00.000 |
| 2021-10-01 00:00:00.000 |
| 2021-10-04 00:00:00.000 |
| 2021-10-02 00:00:00.000 |
| 2021-09-23 00:00:00.000 |
| 2021-10-05 00:00:00.000 |
| 2021-09-26 00:00:00.000 |
| 2021-09-27 00:00:00.000 |
| 2021-10-12 00:00:00.000 |
| 2021-10-09 00:00:00.000 |
| 2021-10-13 00:00:00.000 |
| 2021-10-14 00:00:00.000 |
| 2021-09-30 00:00:00.000 |
| 2021-11-01 00:00:00.000 |
| 2021-10-29 00:00:00.000 |
| 2021-10-15 00:00:00.000 |
| 2021-10-31 00:00:00.000 |
| 2021-10-23 00:00:00.000 |
| 2021-10-20 00:00:00.000 |
| 2021-11-08 00:00:00.000 |
| 2021-10-22 00:00:00.000 |
| 2021-11-15 00:00:00.000 |
| 2021-10-21 00:00:00.000 |
| 2021-11-09 00:00:00.000 |
| 2021-11-13 00:00:00.000 |
| 2021-10-16 00:00:00.000 |
| 2021-11-17 00:00:00.000 |
| 2021-11-16 00:00:00.000 |
| 2021-10-27 00:00:00.000 |
| 2021-11-18 00:00:00.000 |
| 2021-09-13 00:00:00.000 |
| 2021-11-03 00:00:00.000 |
| 2021-09-12 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:
| année-trimestre   |
|:------------------|
| 2020 - t4         |
| 2021 - t1         |
| 2021 - t2         |
| 2021 - t3         |
| 2021 - t4         |
| 2022 - t1         |
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:
| fishery   |
|:----------|
| csf       |
| ecdw      |
| gab       |
| cts       |
| spf       |
| str       |
| etbf      |
| wdtf      |
| nwstf     |
| hmi       |
| mitf      |
| ghat      |
| bsczsf    |
| ssjf      |
| wtbf      |
| sbtf      |
| estf      |
| wstf      |
| npf       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| process                      |
|:-----------------------------|
| csd                          |
| ncb/stills/water             |
| csd/stills/water             |
| leches/bebidas htst          |
| leches/bebidas esl           |
| leches/bebidas uht           |
| yogurt                       |
| helados                      |
| queso                        |
| leches/bebidas esterilizadas |
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:
| labor type   |
|:-------------|
| mechanics    |
| fabricator   |
| welder       |
| carpenters   |
| machinist    |
| cleaners     |
| painters     |
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:
| 星座   |
|:-------|
| 摩羯座 |
| 水瓶座 |
| 雙魚座 |
| 牡羊座 |
| 金牛座 |
| 雙子座 |
| 巨蟹座 |
| 獅子座 |
| 處女座 |
| 天秤座 |
| 天蠍座 |
| 射手座 |
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   |
|:-----|
| a003 |
| a004 |
| a005 |
| a006 |
| a007 |
| a008 |
| a009 |
| a010 |
| a011 |
| a012 |
| a013 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| monthname   |
|:------------|
| july        |
| august      |
| september   |
| april       |
| may         |
| june        |
| october     |
| november    |
| december    |
| january     |
| february    |
| march       |
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:
| year quarter - transaction date   |
|:----------------------------------|
| 2020 q1                           |
| 2020 q2                           |
| 2019 q2                           |
| 2020 q3                           |
| 2020 q4                           |
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:
| floorname    |
|:-------------|
| floor_f_5_25 |
| floor_f_1_11 |
| floor_f_2_12 |
| floor_f_3_13 |
| floor_f_4_14 |
| floor_f_5_15 |
| floor_f_1_16 |
| floor_f_2_17 |
| floor_f_3_18 |
| floor_f_4_19 |
| floor_f_2_2  |
| floor_f_5_20 |
| floor_f_1_21 |
| floor_f_2_22 |
| floor_f_3_23 |
| floor_f_4_24 |
| floor_f_1_1  |
| floor_f_3_3  |
| floor_f_4_4  |
| floor_f_5_5  |
| floor_f_1_6  |
| floor_f_2_7  |
| floor_f_3_8  |
| floor_f_4_9  |
| floor_f_5_10 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| admin_appdescription                                                                                                                                              |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| find colleagues from your organization                                                                                                                            |
| powerapps - app creator portal, for you to manage documentation and communication of your apps                                                                    |
| as a global admin, environment admin or business application admin this app can be used to re-assign app owner roles or add new users to an app.                  |
| chat based application that helps grow our internal community that spans across a global scale.                                                                   |
| portal for powerapps creators to view their apps and submit more information to get better support from their center of excellence.                               |
| embedded canvas app for the powerapps and flow model driven app                                                                                                   |
| an all-in-one meeting capture tool. view meeting details, capture notes and pictures of whiteboards, assign tasks and send to all meeting attendees in one click. |
| university of pennsylvania demo app as of july 1st                                                                                                                |
| scheduling project app.                                                                                                                                           |
| catalog for admins to feature powerapps shared with the organization.                                                                                             |
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:
| chassis_device_name          |
|:-----------------------------|
| chassis-01                   |
| chassis-02                   |
| nhctcore02                   |
| nhctcore01                   |
| usnhctc0001                  |
| modular_switch_01            |
| blade chassis test           |
| test_ucs_chassis             |
| nhctcc01                     |
| usnhcc-dcm01                 |
| usnhcc-dcm02                 |
| lv_b_0                       |
| oa-80c16e7d0bbb              |
| hhiflexchasp03               |
| lv-blade01                   |
| ucspe-10-42-12-247/chassis-3 |
| ucspe-10-42-12-247/chassis-4 |
| ucspe-10-42-12-247/chassis-5 |
| ucspe-10-42-12-247/chassis-6 |
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:
| title                            |
|:---------------------------------|
| maven toys guadalajara 2 💻      |
| maven toys saltillo 1 ✨         |
| maven toys la paz 1 ✨           |
| maven toys mexicali 1 💻         |
| maven toys monterrey 2 ✨        |
| maven toys pachuca 1 ✨          |
| maven toys ciudad de mexico 1 ✨ |
| maven toys campeche 1 ✨         |
| maven toys cuernavaca 1 ✨       |
| maven toys chetumal 1 ✨         |
| maven toys mexicali 2 ✨         |
| maven toys guanajuato 1 ✨       |
| maven toys tuxtla gutierrez 1 ✨ |
| maven toys san luis potosi 1 ✨  |
| maven toys toluca 1 ✨           |
| maven toys merida 1 ✨           |
| maven toys puebla 1 💻           |
| maven toys zacatecas 1 ✨        |
| maven toys santiago 1 ✨         |
| maven toys guanajuato 2 💻       |
| maven toys chihuahua 1 💻        |
| maven toys aguascalientes 1 ✨   |
| maven toys ciudad victoria 1 ✨  |
| maven toys campeche 2 💻         |
| maven toys oaxaca 1 ✨           |
| maven toys puebla 2 ✨           |
| maven toys xalapa 1 💻           |
| maven toys guadalajara 3 🛫      |
| maven toys ciudad de mexico 2 🛫 |
| maven toys hermosillo 1 🏡       |
| maven toys monterrey 3 🛫        |
| maven toys villahermosa 1 ✨     |
| maven toys chilpancingo 1 ✨     |
| maven toys morelia 1 ✨          |
| maven toys ciudad de mexico 3 🏡 |
| maven toys chihuahua 2 ✨        |
| maven toys xalapa 2 ✨           |
| maven toys toluca 2 💻           |
| maven toys hermosillo 2 ✨       |
| maven toys hermosillo 3 💻       |
| maven toys durango 1 ✨          |
| maven toys puebla 3 🏡           |
| maven toys ciudad de mexico 4 💻 |
| maven toys guadalajara 4 ✨      |
| maven toys monterrey 4 💻        |
| maven toys saltillo 2 💻         |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| einsatzart         |
|:-------------------|
| primäreinsätze     |
| sekundäreinsätze   |
| spezielle einsätze |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| month ending            |
|:------------------------|
| 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 |
| 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 |
| 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 |
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:
| activity__c      |
|:-----------------|
| product upsell   |
| price increase   |
| product downsell |
| new product      |
| lost product     |
| new customer     |
| price decrease   |
| lost customer    |
| migration        |
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:
| order date              |
|:------------------------|
| 2019-11-09 00:00:00.000 |
| 2019-04-09 00:00:00.000 |
| 2019-10-23 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
| 2020-05-18 00:00:00.000 |
| 2020-06-17 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| kategorija   |
|:-------------|
| animation    |
| children     |
| classics     |
| comedy       |
| documentary  |
| drama        |
| family       |
| foreign      |
| games        |
| horror       |
| music        |
| new          |
| sci-fi       |
| sports       |
| travel       |
| action       |
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.issues.author_association   |
|:---------------------------------|
| member                           |
| none                             |
| contributor                      |
| collaborator                     |
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                 |
|:------------------------|
| 2020-03-01 00:00:00.000 |
| 2020-04-01 00:00:00.000 |
| 2020-06-01 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2021-01-01 00:00:00.000 |
| 2020-07-01 00:00:00.000 |
| 2020-08-01 00:00:00.000 |
| 2020-09-01 00:00:00.000 |
| 2020-10-01 00:00:00.000 |
| 2020-11-01 00:00:00.000 |
| 2020-12-01 00:00:00.000 |
| 2021-02-01 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 |
| 2021-06-01 00:00:00.000 |
| 2021-07-01 00:00:00.000 |
| 2021-08-01 00:00:00.000 |
| 2021-09-01 00:00:00.000 |
| 2021-10-01 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:
| unit name        |
|:-----------------|
| mymensingh range |
| rangpur range    |
| gmp              |
| rpmp             |
| atu              |
| kmp              |
| bmp              |
| railway range    |
| rajshahi range   |
| barisal range    |
| dhaka range      |
| cmp              |
| chittagong range |
| smp              |
| dmp              |
| sylhet range     |
| khulna range     |
| rmp              |
| total            |
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:
| gen prod posting group   |
|:-------------------------|
| for                      |
| acc                      |
| frt                      |
| ship                     |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| month name   |
|:-------------|
| january      |
| march        |
| may          |
| july         |
| august       |
| october      |
| december     |
| november     |
| september    |
| june         |
| april        |
| february     |
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:
| timestamp               |
|:------------------------|
| 2019-06-27 18:32:27.560 |
| 2019-06-27 18:32:01.403 |
| 2019-06-27 18:32:27.536 |
| 2019-06-27 18:32:27.513 |
| 2019-06-27 18:32:01.446 |
| 2019-06-27 18:32:01.430 |
| 2019-06-27 18:32:01.463 |
| 2019-06-27 18:32:01.490 |
| 2019-06-27 18:32:01.506 |
| 2019-06-27 18:32:01.480 |
| 2019-06-27 18:32:01.536 |
| 2019-06-27 18:32:01.516 |
| 2019-06-27 18:32:01.526 |
| 2019-06-27 18:32:27.583 |
| 2019-06-27 18:32:27.650 |
| 2019-06-27 18:32:27.676 |
| 2019-06-27 18:32:27.710 |
| 2019-06-27 18:32:27.683 |
| 2019-06-27 18:32:27.713 |
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 status   |
|:-------------|
| expired      |
| fulfilled    |
| canceled     |
| active       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| month                   |
|:------------------------|
| 2019-01-01 00:00:00.000 |
| 2019-02-01 00:00:00.000 |
| 2019-03-01 00:00:00.000 |
| 2019-04-01 00:00:00.000 |
| 2019-05-01 00:00:00.000 |
| 2019-06-01 00:00:00.000 |
| 2019-07-01 00:00:00.000 |
| 2019-08-01 00:00:00.000 |
| 2019-09-01 00:00:00.000 |
| 2019-10-01 00:00:00.000 |
| 2019-11-01 00:00:00.000 |
| 2019-12-01 00:00:00.000 |
| 2020-01-01 00:00:00.000 |
| 2020-02-01 00:00:00.000 |
| 2020-03-01 00:00:00.000 |
| 2020-04-01 00:00:00.000 |
| 2020-05-01 00:00:00.000 |
| 2020-06-01 00:00:00.000 |
| 2020-07-01 00:00:00.000 |
| 2020-08-01 00:00:00.000 |
| 2020-09-01 00:00:00.000 |
| 2020-10-01 00:00:00.000 |
| 2020-11-01 00:00:00.000 |
| 2020-12-01 00:00:00.000 |
| 2021-01-01 00:00:00.000 |
| 2021-02-01 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 |
| 2021-06-01 00:00:00.000 |
| 2021-07-01 00:00:00.000 |
| 2021-08-01 00:00:00.000 |
| 2021-09-01 00:00:00.000 |
| 2021-10-01 00:00:00.000 |
| 2021-11-01 00:00:00.000 |
| 2021-12-01 00:00:00.000 |
| 2018-01-01 00:00:00.000 |
| 2018-02-01 00:00:00.000 |
| 2018-03-01 00:00:00.000 |
| 2018-04-01 00:00:00.000 |
| 2018-05-01 00:00:00.000 |
| 2018-06-01 00:00:00.000 |
| 2018-07-01 00:00:00.000 |
| 2018-08-01 00:00:00.000 |
| 2018-09-01 00:00:00.000 |
| 2018-10-01 00:00:00.000 |
| 2018-11-01 00:00:00.000 |
| 2018-12-01 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:
| matches played   |
|:-----------------|
| final            |
| retired          |
| walkover         |