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:
| team logo                                                   |
|:------------------------------------------------------------|
| https://cdn.nba.com/logos/nba/1610612766/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612751/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612755/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612752/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612747/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612745/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612743/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612759/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612765/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612749/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612744/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612757/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612758/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612761/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612738/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612764/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612742/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612762/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612753/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612756/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612750/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612741/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612737/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612760/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612746/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612763/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612754/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612740/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612739/primary/l/logo.svg |
| https://cdn.nba.com/logos/nba/1610612748/primary/l/logo.svg |
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:
| storage location code   |
|:------------------------|
| cw03                    |
| cw05                    |
| cs01                    |
| cw07                    |
| cw06                    |
| cs02                    |
| cs04                    |
| cs03                    |
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:
| kode aset   |
|:------------|
| pnre-004    |
| x001        |
| f309        |
| g202        |
| f123        |
| f440        |
| f425        |
| f409        |
| f404        |
| f318        |
| f306        |
| f213        |
| f211        |
| f210        |
| g201        |
| f207        |
| f205        |
| f204        |
| f202        |
| f201        |
| f126        |
| f125        |
| f121        |
| f114        |
| f113        |
| f110        |
| f101        |
| a316        |
| a103        |
| fl-029      |
| fl-033      |
| fl-031      |
| fl-056      |
| fl-055      |
| fl-034      |
| fl-060      |
| fl-005      |
| fl-010      |
| fl-050      |
| fl-009      |
| fl-059      |
| fl-048      |
| fl-039      |
| fl-072      |
| fl-078      |
| fl-038      |
| fl-008      |
| fl-007      |
| fl-004      |
| fl-003      |
| fl-002      |
| fl-001      |
| gas-020     |
| pnre-003    |
| r601        |
| r201        |
| r202        |
| f215        |
| g207        |
| f441        |
| f432        |
| f420        |
| f416        |
| f413        |
| f403        |
| f307        |
| f304        |
| f216        |
| f214        |
| a453        |
| a452        |
| a403        |
| a325        |
| a322        |
| a314        |
| a306        |
| a237        |
| a207        |
| a205        |
| a204        |
| a203        |
| a194        |
| a191        |
| a132        |
| a105        |
| fl-065      |
| fl-028      |
| fl-068      |
| fl-054      |
| fl-058      |
| fl-051      |
| fl-047      |
| gas-019     |
| gas-016     |
| gas-021     |
| pnre-005    |
| pnre-001    |
| pnre-002    |
| r701        |
| f414        |
| r401        |
| r301        |
| g501        |
| f419        |
| f402        |
| f322        |
| f303        |
| f109        |
| f108        |
| f107        |
| f106        |
| a428        |
| a419        |
| a206        |
| fl-061      |
| fl-049      |
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                    |
|:------------------------|
| 2021-04-28 00:00:00.000 |
| 2021-05-01 00:00:00.000 |
| 2021-05-02 00:00:00.000 |
| 2021-05-03 00:00:00.000 |
| 2021-05-04 00:00:00.000 |
| 2021-05-06 00:00:00.000 |
| 2021-05-07 00:00:00.000 |
| 2021-05-10 00:00:00.000 |
| 2021-05-11 00:00:00.000 |
| 2021-04-01 00:00:00.000 |
| 2021-04-03 00:00:00.000 |
| 2021-04-05 00:00:00.000 |
| 2021-04-06 00:00:00.000 |
| 2021-04-07 00:00:00.000 |
| 2021-04-08 00:00:00.000 |
| 2021-04-09 00:00:00.000 |
| 2021-04-10 00:00:00.000 |
| 2021-04-11 00:00:00.000 |
| 2021-04-12 00:00:00.000 |
| 2021-04-13 00:00:00.000 |
| 2021-04-14 00:00:00.000 |
| 2021-04-15 00:00:00.000 |
| 2021-04-16 00:00:00.000 |
| 2021-04-17 00:00:00.000 |
| 2021-04-18 00:00:00.000 |
| 2021-04-19 00:00:00.000 |
| 2021-04-20 00:00:00.000 |
| 2021-04-21 00:00:00.000 |
| 2021-04-22 00:00:00.000 |
| 2021-04-23 00:00:00.000 |
| 2021-04-24 00:00:00.000 |
| 2021-04-25 00:00:00.000 |
| 2021-04-26 00:00:00.000 |
| 2021-04-27 00:00:00.000 |
| 2021-04-29 00:00:00.000 |
| 2021-04-30 00:00:00.000 |
| 2021-05-05 00:00:00.000 |
| 2021-05-08 00:00:00.000 |
| 2021-05-09 00:00:00.000 |
| 2021-05-12 00:00:00.000 |
| 2021-05-13 00:00:00.000 |
| 2021-05-14 00:00:00.000 |
| 2021-05-15 00:00:00.000 |
| 2021-05-16 00:00:00.000 |
| 2021-05-17 00:00:00.000 |
| 2021-04-02 00:00:00.000 |
| 2021-04-04 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:
| descricao   |
|:------------|
| 28 dias     |
| 30/45 dias  |
| 30 dias     |
| 45 dias     |
| 21 dias     |
| 30/45/60    |
| 14 dias     |
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:
| how many people are currently employed by your organisation?   |
|:---------------------------------------------------------------|
| more than 200                                                  |
| between 5 and 19                                               |
| between 20 and 199                                             |
| between 1 and 4                                                |
| i am the only employee                                         |
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_label_long   |
|:-------------------|
| november           |
| october            |
| june               |
| september          |
| december           |
| july               |
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:
| grupo de edad   |
|:----------------|
| 00 - 04 años    |
| 05 - 09 años    |
| 10 - 14 años    |
| 15 - 19 años    |
| 20 - 24 años    |
| 25 - 29 años    |
| 30 - 34 años    |
| 35 - 39 años    |
| 40 - 44 años    |
| 45 - 49 años    |
| 50 - 54 años    |
| 55 - 59 años    |
| 60 - 64 años    |
| 65 - 69 años    |
| 70 - 74 años    |
| 75 - 79 años    |
| 80 y más años   |
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   |
|:-----------|
| tzs        |
| kes        |
| rwf        |
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                    |
|:------------------------|
| 2000-01-02 00:00:00.000 |
| 2000-01-03 00:00:00.000 |
| 2000-01-04 00:00:00.000 |
| 2000-01-05 00:00:00.000 |
| 2000-01-06 00:00:00.000 |
| 2000-01-07 00:00:00.000 |
| 2000-01-08 00:00:00.000 |
| 2000-01-09 00:00:00.000 |
| 2000-01-10 00:00:00.000 |
| 2000-01-11 00:00:00.000 |
| 2000-01-12 00:00:00.000 |
| 2000-01-13 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:
| bill_number   |
|:--------------|
| b589965       |
| b589844       |
| b589741       |
| b589609       |
| b589215       |
| b589771       |
| b589627       |
| b589625       |
| b589356       |
| b589355       |
| b589213       |
| b589530       |
| b589529       |
| b589569       |
| b586788       |
| b589448       |
| b589444       |
| b589353       |
| b589103       |
| b587165       |
| b589365       |
| b589342       |
| b586794       |
| b588794       |
| b589252       |
| b588045       |
| b589251       |
| b589359       |
| b589102       |
| b589211       |
| b588793       |
| b589101       |
| b588792       |
| b589112       |
| b589111       |
| b589210       |
| b589179       |
| b589054       |
| b588791       |
| b587542       |
| b587541       |
| b588866       |
| b588980       |
| b588981       |
| b589026       |
| b588790       |
| b588899       |
| b588849       |
| b588850       |
| b588858       |
| b588907       |
| b588046       |
| b588848       |
| b588831       |
| b587540       |
| b587163       |
| b587769       |
| b587538       |
| b587768       |
| b587539       |
| b588674       |
| b588675       |
| b588676       |
| b587537       |
| b588047       |
| b588592       |
| b587766       |
| b587767       |
| b588556       |
| b588557       |
| b587536       |
| b588477       |
| b587547       |
| b588476       |
| b588398       |
| b589642       |
| b588073       |
| b589380       |
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:
| job level before fy20 promotions   |
|:-----------------------------------|
| 6 - junior officer                 |
| 4 - manager                        |
| 5 - senior officer                 |
| 2 - director                       |
| 3 - senior manager                 |
| 1 - executive                      |
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   |
|:--------|
| nsw     |
| vic     |
| qld     |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| region         |
|:---------------|
| france         |
| uk             |
| russia         |
| italy          |
| canada         |
| japan          |
| sweden         |
| australia      |
| hungary        |
| czech republic |
| poland         |
| switzerland    |
| netherlands    |
| china          |
| finland        |
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:
| posting date            |
|:------------------------|
| 2017-01-27 00:00:00.000 |
| 2017-01-30 00:00:00.000 |
| 2017-02-06 00:00:00.000 |
| 2017-02-03 00:00:00.000 |
| 2017-02-20 00:00:00.000 |
| 2017-02-21 00:00:00.000 |
| 2017-03-16 00:00:00.000 |
| 2017-03-14 00:00:00.000 |
| 2017-05-04 00:00:00.000 |
| 2017-05-31 00:00:00.000 |
| 2017-06-01 00:00:00.000 |
| 2017-06-07 00:00:00.000 |
| 2017-07-05 00:00:00.000 |
| 2017-07-13 00:00:00.000 |
| 2017-07-19 00:00:00.000 |
| 2017-09-01 00:00:00.000 |
| 2017-09-06 00:00:00.000 |
| 2017-10-11 00:00:00.000 |
| 2017-11-08 00:00:00.000 |
| 2017-11-29 00:00:00.000 |
| 2017-12-20 00:00:00.000 |
| 2017-12-22 00:00:00.000 |
| 2018-01-10 00:00:00.000 |
| 2018-01-23 00:00:00.000 |
| 2018-01-26 00:00:00.000 |
| 2018-01-31 00:00:00.000 |
| 2018-02-21 00:00:00.000 |
| 2018-02-27 00:00:00.000 |
| 2018-02-28 00:00:00.000 |
| 2018-03-08 00:00:00.000 |
| 2018-04-17 00:00:00.000 |
| 2018-04-18 00:00:00.000 |
| 2018-05-08 00:00:00.000 |
| 2018-06-06 00:00:00.000 |
| 2018-06-25 00:00:00.000 |
| 2018-07-03 00:00:00.000 |
| 2018-07-20 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:
| assignmentid                         |
|:-------------------------------------|
| f9bde4fe-e979-4c0b-ad97-ea6acd5244f0 |
| e2b2bd65-59ee-4c59-b32a-198096ec9ce7 |
| 692916c0-9f8d-4b0d-843a-a0e797525a0b |
| feb22fc9-f2b7-42f4-adf1-b9588b11d889 |
| 2103cf4e-c253-4eda-993b-065b0c48834e |
| 8e1f95de-5d75-4401-9c56-4d466c7eb0a8 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| site town   |
|:------------|
| manchester  |
| glasgow     |
| portsmouth  |
| belfast     |
| plymouth    |
| london      |
| liverpool   |
| exeter      |
| birmingham  |
| southampton |
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:
| dia txt   |
|:----------|
| fri       |
| sat       |
| sun       |
| mon       |
| tue       |
| wed       |
| thu       |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| nota_de_credito   |
|:------------------|
| 19/2021           |
| 21/2021           |
| 23/2021           |
| 25/2021           |
| 27/2021           |
| 31/2021           |
| 43/2021           |
| 44/2021           |
| 58/2021           |
| 61/2021           |
| 69/2021           |
| 10/2021           |
| 15/2021           |
| 02/2021           |
| 03/2021           |
| 04/2021           |
| 05/2021           |
| 06/2021           |
| 07/2021           |
| 08/2021           |
| 09/2021           |
| 11/2021           |
| 12/2021           |
| 13/2021           |
| 14/2021           |
| 16/2021           |
| 17/2021           |
| 18/2021           |
| 20/2021           |
| 22/2021           |
| 24/2021           |
| 26/2021           |
| 28/2021           |
| 29/2021           |
| 30/2021           |
| 32/2021           |
| 33/2021           |
| 34/2021           |
| 35/2021           |
| 36/2021           |
| 37/2021           |
| 38/2021           |
| 39/2021           |
| 40/2021           |
| 41/2021           |
| 42/2021           |
| 45/2021           |
| 46/2021           |
| 47/2021           |
| 48/2021           |
| 49/2021           |
| 50/2021           |
| 51/2021           |
| 52/2021           |
| 53/2021           |
| 54/2021           |
| 55/2021           |
| 56/2021           |
| 57/2021           |
| 59/2021           |
| 60/2021           |
| 62/2021           |
| 63/2021           |
| 64/2021           |
| 65/2021           |
| 66/2021           |
| 67/2021           |
| 68/2021           |
| 70/2021           |
| 71/2021           |
| 72/2021           |
| 73/2021           |
| 74/2021           |
| 75/2021           |
| 76/2021           |
| 77/2021           |
| 01/2021           |
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:
| updated on              |
|:------------------------|
| 2020-10-15 10:16:27.393 |
| 2020-10-08 11:31:10.073 |
| 2020-10-20 12:42:36.930 |
| 2020-11-05 14:20:13.510 |
| 2020-10-22 08:28:38.040 |
| 2020-11-05 14:25:00.196 |
| 2020-11-04 07:49:46.536 |
| 2020-11-18 07:47:23.930 |
| 2020-11-18 07:48:26.710 |
| 2020-11-18 07:50:52.470 |
| 2020-11-13 08:12:01.900 |
| 2020-11-13 08:08:06.486 |
| 2020-12-02 08:17:17.246 |
| 2020-10-26 15:18:55.793 |
| 2020-12-02 09:24:51.183 |
| 2020-10-27 15:52:56.320 |
| 2020-12-02 08:35:22.253 |
| 2020-12-02 08:36:17.106 |
| 2020-10-27 15:50:18.840 |
| 2020-11-05 14:52:21.506 |
| 2020-11-05 14:54:14.056 |
| 2020-12-02 08:39:13.056 |
| 2020-12-02 08:41:55.973 |
| 2020-12-02 08:43:13.793 |
| 2020-12-02 08:45:30.523 |
| 2020-12-02 08:46:37.760 |
| 2020-12-09 13:03:41.963 |
| 2020-11-12 07:29:31.570 |
| 2020-12-07 09:13:01.476 |
| 2020-12-07 09:23:57.406 |
| 2020-12-08 07:27:43.860 |
| 2020-11-11 12:04:35.676 |
| 2020-11-11 12:11:30.753 |
| 2020-11-16 11:31:42.736 |
| 2020-12-08 07:40:27.193 |
| 2021-01-06 10:07:10.056 |
| 2021-01-06 13:13:25.913 |
| 2020-11-17 14:53:40.140 |
| 2020-12-07 09:10:36.020 |
| 2020-12-02 09:16:00.163 |
| 2020-12-07 09:12:07.266 |
| 2020-12-10 08:16:51.526 |
| 2021-01-06 08:49:49.600 |
| 2020-12-07 09:17:53.113 |
| 2020-12-07 15:11:13.940 |
| 2020-12-09 07:31:41.563 |
| 2021-01-20 12:47:17.060 |
| 2021-01-20 12:47:04.033 |
| 2021-01-06 08:01:37.866 |
| 2021-01-15 12:21:04.903 |
| 2021-01-07 13:57:20.006 |
| 2021-01-12 06:50:41.443 |
| 2021-01-05 14:39:49.386 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| dayofweekname   |
|:----------------|
| thursday        |
| friday          |
| saturday        |
| sunday          |
| monday          |
| tuesday         |
| wednesday       |
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:
| resp content   |
|:---------------|
| resp2          |
| resp3          |
| resp5          |
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:
| deadline                |
|:------------------------|
| 2020-11-17 17:05:56.000 |
| 2020-09-11 18:00:00.000 |
| 2020-09-09 18:00:00.000 |
| 2020-11-03 15:15:00.000 |
| 2020-11-11 17:05:50.000 |
| 2020-11-19 13:19:00.000 |
| 2020-12-08 17:48:58.000 |
| 2020-11-17 17:05:59.000 |
| 2020-09-12 01:00:00.000 |
| 2020-11-14 02:01:00.000 |
| 2020-09-11 01:00:00.000 |
| 2020-09-13 01:00:00.000 |
| 2020-11-13 22:01:00.000 |
| 2020-11-15 02:01:00.000 |
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:
| fiscal year-quarter   |
|:----------------------|
| 2021q01               |
| 2021q02               |
| 2021q03               |
| 2021q04               |
| 2022q02               |
| 2022q01               |
| 2020q04               |
| 2020q03               |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| quarter   |
|:----------|
| q3        |
| q4        |
| q1        |
| q2        |
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:
| frecuencia_agrede   |
|:--------------------|
| intermitente        |
| semanal             |
| diario              |
| quincenal           |
| mensual             |
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:
| sourcenamedetail                 |
|:---------------------------------|
| eurostat main database           |
| ifs 2020                         |
| national statistics france       |
| national statistics japan        |
| national statistics spain        |
| national statistics uk           |
| national statistics usa          |
| oecd fixed assets                |
| oecd fixed assets archive        |
| oecd gdp and wages               |
| oecd gdp and wages archive       |
| oecd national accounts           |
| oecd simplified non-financial    |
| 2019 dataset                     |
| 2020 penn national accounts data |
| 2020 penn world tables           |
| 2020 penn capital stock          |
| un 2018                          |
| un 2020                          |
| un2018 national agencies         |
| wdi 2020                         |
| weo 2002                         |
| weo 2020                         |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| vaccines                              |
|:--------------------------------------|
| pfizer/biontech                       |
| moderna, pfizer/biontech              |
| sputnik v                             |
| pfizer/biontech, sinopharm            |
| cnbg, sinovac                         |
| sinopharm                             |
| sinovac                               |
| oxford/astrazeneca, sinopharm         |
| oxford/astrazeneca                    |
| pfizer/biontech, sinovac              |
| pfizer/biontech, sinopharm, sputnik v |
| oxford/astrazeneca, pfizer/biontech   |
| covaxin, oxford/astrazeneca           |
| oxford/astrazeneca, sinovac           |
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:
| период   |
|:---------|
| фев 18   |
| мар 18   |
| апр 18   |
| май 18   |
| июн 18   |
| июл 18   |
| авг 18   |
| сен 18   |
| окт 18   |
| ноя 18   |
| дек 18   |
| янв 19   |
| фев 19   |
| мар 19   |
| апр 19   |
| май 19   |
| июн 19   |
| июл 19   |
| авг 19   |
| сен 19   |
| окт 19   |
| ноя 19   |
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                    |
|:------------------------|
| 2021-07-03 00:00:00.000 |
| 2021-07-04 00:00:00.000 |
| 2021-07-05 00:00:00.000 |
| 2021-07-06 00:00:00.000 |
| 2021-07-07 00:00:00.000 |
| 2021-07-08 00:00:00.000 |
| 2021-07-09 00:00:00.000 |
| 2021-07-10 00:00:00.000 |
| 2021-07-11 00:00:00.000 |
| 2021-08-01 00:00:00.000 |
| 2021-08-02 00:00:00.000 |
| 2021-08-03 00:00:00.000 |
| 2021-08-04 00:00:00.000 |
| 2021-08-05 00:00:00.000 |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| principais indicadores e temas avaliados              |
|:------------------------------------------------------|
| processo de escalonamento para incidentes             |
| integração com o sistema de avaliação de funcionários |
| frequencia de teste do plano de contingência          |
| certificação                                          |
| treinamento para funcionários                         |
Task: Error-Detect (source dataset: Relational-Tables)

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

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

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

Column:
| area            |
|:----------------|
| alpine          |
| sierra          |
| mariposa        |
| tehama          |
| glenn           |
| modoc           |
| trinity         |
| colusa          |
| lassen          |
| amador          |
| inyo            |
| kings           |
| lake            |
| madera          |
| plumas          |
| sutter          |
| siskiyou        |
| yuba            |
| mono            |
| san benito      |
| calaveras       |
| imperial        |
| monterey        |
| tuolumne        |
| butte           |
| del norte       |
| humboldt        |
| mendocino       |
| nevada          |
| yolo            |
| napa            |
| shasta          |
| merced          |
| san luis obispo |
| el dorado       |
| santa barbara   |
| santa cruz      |
| san bernardino  |
| tulare          |
| stanislaus      |
| fresno          |
| kern            |
| sonoma          |
| marin           |
| san joaquin     |
| solano          |
| placer          |
| ventura         |
| orange          |
| alameda         |
| contra costa    |
| riverside       |
| sacramento      |
| san diego       |
| san francisco   |
| san mateo       |
| los angeles     |
| santa clara     |
| california      |
| out of state    |