MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Spreadsheet-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:
| c                   |
|:--------------------|
| matt sanchez        |
| gavin barnick       |
| braden peiser       |
| derek keiser        |
| andrew duryea       |
| parker haydin       |
| john blanton, iii   |
| scott rockett       |
| andrew  duross      |
| john kersey         |
| griffin lake        |
| dylan gregory       |
| logan michael       |
| scott patterson     |
| philip becker       |
| mason hamilton      |
| gavin perkowski     |
| ryan kim            |
| sean webb           |
| tyler wee           |
| grant kimberling    |
| jacob day           |
| dante aletto        |
| ryan wee            |
| tanner barth        |
| bryce ward          |
| aaron hoversten     |
| joel potts          |
| hayden bell         |
| cory stroupe        |
| briggs moore        |
| sawyer williams     |
| logan mcclimans     |
| david clark         |
| joseph hahn         |
| anthony james hotko |
| anthony kissik      |
| alok  joarder       |
| theodore clark      |
| elias haddad        |
| zachary osvald      |
| spencer gillis      |
| joshua potts        |
| eli guise           |
| matthew herrington  |
| cooper gray         |
| stephen mcafee      |
| thomas kirsten      |
| zachary lee jackson |
| carson wood         |
| jackson wall        |
| samuel sparrow      |
| ben tafoya          |
| marcus klemp        |
| dylan zaun          |
| ben roche           |
| gatlin james        |
| jack ogoreuc        |
| garrett langerud    |
| zachary byers       |
| marshall schmidt    |
| parker sullivan     |
| bryan kuklo         |
| hunter root         |
| logan doucette      |
Task: Error-Detect (source dataset: Spreadsheet-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:
| section                           |
|:----------------------------------|
| budgetary resources               |
| status of budgetary resources     |
| change in obligated balance       |
| budget authority and outlays_ net |
Task: Error-Detect (source dataset: Spreadsheet-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:
| Unnamed: 0   |
|:-------------|
| t002         |
| t003         |
| t004         |
| t005         |
| t006         |
| t007         |
| t008         |
| t009         |
| t010         |
| t011         |
| t012         |
| t013         |
| t014         |
| t015         |
| t016         |
| t017         |
| t018         |
| t019         |
Task: Error-Detect (source dataset: Spreadsheet-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:
| m            |
|:-------------|
| fort wayne   |
| huntertown   |
| auburn       |
| new haven    |
| monroeville  |
| hoagland     |
| woodburn     |
| harlan       |
| churubusco   |
| grabill      |
| arcola       |
| spencerville |
| roanoke      |
Task: Error-Detect (source dataset: Spreadsheet-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:
| first       |
|:------------|
| niven       |
| dan         |
| jay antonio |
| zachary     |
| leoner      |
| darion      |
| david       |
| fernando    |
Task: Error-Detect (source dataset: Spreadsheet-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:
| gruppering                                  |
|:--------------------------------------------|
| enskilda basspecialiteter                   |
| ingen specialitet vald                      |
| tilläggsspecialiteter                       |
| bild- och funktionsmedicinska specialiteter |
| neurospeciliteter                           |
| barnmedicinska specialiteter                |
| kirurgiska specialiteter                    |
| invärtesmedicinska specialiteter            |
| psykiatriska specialiteter                  |
Task: Error-Detect (source dataset: Spreadsheet-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:
| code group          |
|:--------------------|
| demographic codes   |
| event codes         |
| process based codes |
| outcome based codes |
| root cause codes    |
Task: Error-Detect (source dataset: Spreadsheet-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:
| geogr_area_id   |
|:----------------|
| 1009            |
| il              |
| ps              |
| 1032            |
| 1033            |
| 1034            |
| 2005            |
| 2080            |
| 2200            |
| ad              |
| cm              |
| co              |
| eg              |
| fj              |
| ge              |
| jo              |
| kr              |
| lb              |
| ma              |
| md              |
| pe              |
| pg              |
| sm              |
| tr              |
| ua              |
| 1011            |
| ch              |
| xs              |
| me              |
| xk              |
| ba              |
| mk              |
| 9b02            |
| 9b03            |
| ar              |
| au              |
| ca              |
| nz              |
| us              |
| uy              |
| cl              |
| br              |
| py              |
| gl              |
| is              |
| 2500            |
| al              |
| 1008            |
| 9v03            |
| th              |
| 2012            |
| no              |
| pm              |
| za              |
| 9l04            |
| 9e09            |
| 9e10            |
| 1007            |
| 2020            |
| mx              |
| cn              |
| 2027            |
| dz              |
| ec              |
| tn              |
| id              |
| bd              |
| in              |
| pk              |
| gy              |
| mg              |
| mu              |
| sc              |
| cv              |
| bb              |
| bf              |
| bj              |
| bz              |
| cd              |
| ci              |
| do              |
| et              |
| jm              |
| ke              |
| kh              |
| la              |
| mm              |
| mw              |
| mz              |
| np              |
| sd              |
| sl              |
| sn              |
| sz              |
| tg              |
| tt              |
| tz              |
| ug              |
| zm              |
| cw              |
| cu              |
| li              |
| fo              |
| 2301            |
| 2300            |
| sv              |
| pa              |
| cr              |
| ni              |
| gt              |
| hn              |
Task: Error-Detect (source dataset: Spreadsheet-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:
| letb   |
|:-------|
| heem   |
| heeoe  |
| hekss  |
| hencel |
| hene   |
| henw   |
| henwl  |
| hesl   |
| hesw   |
| hetv   |
| hewm   |
| hewsx  |
| heyh   |
Task: Error-Detect (source dataset: Spreadsheet-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:
| description      |
|:-----------------|
| sugar            |
| stirrer          |
| cups             |
| cup lids         |
| marketing sleeve |
| water            |
Task: Error-Detect (source dataset: Spreadsheet-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:
| group 1   |
|:----------|
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| g63       |
| g64       |
| g65       |
| g66       |
| g67       |
| g68       |
| g69       |
Task: Error-Detect (source dataset: Spreadsheet-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:
|          k |
|-----------:|
| 0.026803   |
| 0.0266644  |
| 0.0264796  |
| 0.023974   |
| 0.038316   |
| 0.0289669  |
| 0.0308663  |
| 0.0309575  |
| 0.0343403  |
| 0.0315958  |
| 0.0250899  |
| 0.0242998  |
| 0.0223168  |
| 0.026017   |
| 0.0229713  |
| 0.0210509  |
| 0.0238576  |
| 0.0215672  |
| 0.0199924  |
| 0.0195209  |
| 0.0198039  |
| 0.020275   |
| 0.0160113  |
| 0.0190251  |
| 0.0181972  |
| 0.015176   |
| 0.0149609  |
| 0.0185286  |
| 0.0158206  |
| 0.0168446  |
| 0.0186233  |
| 0.0151043  |
| 0.0172248  |
| 0.0145302  |
| 0.014626   |
| 0.0129464  |
| 0.0152955  |
| 0.0151999  |
| 0.0155581  |
| 0.0184103  |
| 0.0171298  |
| 0.0154149  |
| 0.0154388  |
| 0.015749   |
| 0.0164639  |
| 0.0151521  |
| 0.0130907  |
| 0.0154626  |
| 0.0165591  |
| 0.0137152  |
| 0.0142428  |
| 0.0136912  |
| 0.0137872  |
| 0.0146978  |
| 0.0138352  |
| 0.0132349  |
| 0.0146738  |
| 0.014075   |
| 0.0107746  |
| 0.0126817  |
| 0.0123686  |
| 0.0124409  |
| 0.013331   |
| 0.0112826  |
| 0.0108472  |
| 0.0115967  |
| 0.0119829  |
| 0.00970772 |
| 0.0121035  |
| 0.0120552  |
| 0.0115243  |
| 0.0105324  |
| 0.00963486 |
| 0.00975629 |
| 0.0105567  |
| 0.0116209  |
| 0.0121276  |
| 0.0122963  |
| 0.0114035  |
| 0.0137632  |
| 0.0144345  |
| 0.014123   |
| 0.0128742  |
| 0.0140271  |
| 0.0143866  |
| 0.0140511  |
| 0.0157967  |
| 0.0147935  |
| 0.0129224  |
| 0.0130667  |
| 0.0138592  |
| 0.0123927  |
| 0.0134031  |
| 0.013259   |
| 0.0148174  |
| 0.0134271  |
| 0.013283   |
| 0.012465   |
| 0.0128021  |
| 0.0120311  |
| 0.0125132  |
| 0.0123204  |
| 0.0116933  |
| 0.0102416  |
| 0.00863745 |
| 0.0100961  |
| 0.0122481  |
| 0.012778   |
| 0.0112101  |
| 0.0111859  |
| 0.012007   |
| 0.0125613  |
| 0.0104355  |
| 0.0114759  |
| 0.0121999  |
| 0.0118623  |
| 0.012224   |
| 0.0126336  |
| 0.0117416  |
| 0.0109682  |
| 0.0121758  |
| 0.0115484  |
| 0.0102658  |
| 0.0111134  |
| 0.0111375  |
Task: Error-Detect (source dataset: Spreadsheet-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:
| Unnamed: 0                                      |
|:------------------------------------------------|
| people have a safe and affordable place to live |
| effective and efficient legal system            |
| reduce reoffending                              |
| safer communities                               |
Task: Error-Detect (source dataset: Spreadsheet-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:
|         h |
|----------:|
| 0.060345  |
| 0.0609172 |
| 0.060865  |
| 0.0600843 |
| 0.0602058 |
| 0.0604428 |
| 0.0606779 |
| 0.0599535 |
| 0.0604028 |
| 0.0597321 |
| 0.0602746 |
| 0.0598496 |
| 0.0603606 |
| 0.060178  |
| 0.060992  |
| 0.060539  |
| 0.0596012 |
| 0.0592742 |
| 0.0604893 |
| 0.0603292 |
| 0.0594978 |
| 0.0594815 |
| 0.0603344 |
| 0.0604323 |
| 0.0596823 |
| 0.0589595 |
| 0.0592485 |
| 0.0604251 |
| 0.0597429 |
| 0.0601959 |
Task: Error-Detect (source dataset: Spreadsheet-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:
| vdc            |
|:---------------|
| chaurikharka   |
| goli           |
| jubing         |
| khumjung       |
| namche         |
| salleri        |
| waku           |
| bhakanje       |
| kureng         |
| necha batase   |
| necha bedghari |
| pawai          |
| salyan         |
| bung           |
| chheskam       |
| gudel          |
| sotang         |
Task: Error-Detect (source dataset: Spreadsheet-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:
| toetsen              |
|:---------------------|
| ctrl + f4            |
| ctrl + w             |
| windows + l          |
| windows + m          |
| alt + f4             |
| ctrl + s             |
| f12                  |
| ctrl + p             |
| ctrl + muiswiel      |
| f7                   |
| alt + 6              |
| ctrl + shift         |
| f1                   |
| alt                  |
| rechtsklikken        |
| menu-toets           |
| ctrl + pijltjes      |
| ctrl + home          |
| ctrl + end           |
| ctrl + page up       |
| ctrl + pagedown      |
| shift + navigeren    |
| ctrl + spatiebalk    |
| shift + spatiebalk   |
| ctrl + a             |
| tab                  |
| shift + tab          |
| ctrl + tab           |
| alt + tab            |
| ctrl + shift + o     |
| alt + =              |
| ctrl + ;             |
| ctrl + :             |
| ctrl + t             |
| shift + f11          |
| ctrl + enter         |
| alt + enter          |
| ctrl + shift + enter |
| alt + pijl omlaag    |
| shift + f2           |
| f2                   |
| ctrl + c             |
| ctrl + x             |
| ctrl + v             |
| ctrl + z             |
| ctrl + y             |
| ctrl + f             |
| shift + f5           |
| shift + f4           |
| ctrl + h             |
| ctrl + g             |
| f5                   |
| f4                   |
| f3                   |
| alt + f8             |
| alt + f11            |
| ctrl + b             |
| ctrl + i             |
| ctrl + u             |
| ctrl + 1             |
| ctrl + shift + 7     |
| ctrl + shift + 1     |
| ctrl + shift + 2     |
| ctrl + shift + 3     |
| ctrl + shift + 4     |
| ctrl + shift + 5     |
| ctrl + +             |
Task: Error-Detect (source dataset: Spreadsheet-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:
| es             |
|:---------------|
| met            |
| not applicable |
| not met        |
Task: Error-Detect (source dataset: Spreadsheet-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:
| initials   |
|:-----------|
| wj         |
| d          |
| cj         |
| c          |
| w          |
| e          |
| lw         |
| fw         |
| jj         |
| sj         |
| pd         |
| jm         |
| p          |
| l          |
| aw         |
| tv         |
| kb         |
| f          |
| s          |
| sl         |
| ra         |
| gc         |
| ww         |
| sm         |
| gm         |
| hg         |
| pj         |
| dj         |
| pm         |
| n          |
| iz         |
| hc         |
| i          |
| gw         |
| lr         |
| id         |
| j          |
| a          |
| tp         |
| jp         |
| fc         |
| ma         |
| cr         |
| b          |
| g          |
| ep         |
| la         |
| oj         |
| npk        |
| aj         |
| ajp        |
| gb         |
| rr         |
| lt         |
| js         |
| sa         |
| cmc        |
| m          |
| bk         |
| pc         |
| gj         |
| lm         |
| ph         |
| r          |
| dh         |
| rj         |
| h          |
| hma        |
| as         |
| hwa        |
| mj         |
| g-j        |
| mt         |
| jb         |
| ka         |
| ge         |
| gh         |
| hj         |
| sc         |
| ce         |
| baj        |
| eg         |
| cm         |
| dl         |
| ga         |
| sn         |
| dr         |
| tf         |
| gms        |
| fj         |
| jpa        |
| jh         |
| gk         |
| jl         |
| dc         |
| ja         |
| ac         |
| bd         |
| rp         |
| on         |
| hr         |
| fhw        |
| ot         |
| gf         |
| we         |
| lj         |
| rt         |
| ch         |
| rh         |
| hm         |
| bs         |
Task: Error-Detect (source dataset: Spreadsheet-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:
| nombre de la unidad responsable                             |
|:------------------------------------------------------------|
| dirección de recursos humanos y organización                |
| despacho de la subdirección general de imagen y vinculación |
| despacho de la subdirección general zona norte              |
| dirección de noticias                                       |
| dirección de televisión                                     |
| despacho de la subdirección general de administración       |
| dirección de contabilidad y auditoría                       |
| dirección técnica                                           |
| dirección de producción y operación                         |
| dirección de radio                                          |
| despacho de la dirección general del s. q. c. s.            |
| dirección de sistemas e informática                         |
| dirección de seguimiento gubernamental                      |
| despacho de la subdirección general de radio                |
| dirección jurídica                                          |
| dirección de producciónes especiales                        |
Task: Error-Detect (source dataset: Spreadsheet-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:
| section                           |
|:----------------------------------|
| budget authority and outlays_ net |
| budgetary resources               |
| change in obligated balance       |
| status of budgetary resources     |
Task: Error-Detect (source dataset: Spreadsheet-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:
| top words in tweet in g1   |
|:---------------------------|
| official                   |
| hoosierarmy17              |
| iubb                       |
| coach                      |
| allen                      |
Task: Error-Detect (source dataset: Spreadsheet-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:
| d   |
|:----|
| bx  |
| pk  |
| dz  |
| se  |
| rm  |
| ea  |
| ct  |
| ro  |
Task: Error-Detect (source dataset: Spreadsheet-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:
| столбец3                  |
|:--------------------------|
| русский  язык             |
| изобразительное искусство |
| окружающий мир            |
| математика                |
| физическая культура       |
| литературное чтение       |
| технология                |
| музыка                    |
Task: Error-Detect (source dataset: Spreadsheet-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:
| subject 1 legend                     |
|:-------------------------------------|
| law                                  |
| european history                     |
| english literature                   |
| film_ media_ mass communication      |
| european and world literature        |
| psychology                           |
| physics                              |
| religion                             |
| anthropology                         |
| history cross discipline             |
| british history                      |
| american history                     |
| history other regions                |
| earth and environmental sciences     |
| mathematics                          |
| medicine                             |
| computer science                     |
| astronomy                            |
| music                                |
| classical studies                    |
| statistics and probability           |
| art                                  |
| politics and international relations |
| american literature                  |
| philosophy                           |
| drama and theatre                    |
| life sciences                        |
| economics                            |
| books for teachers                   |
| language and linguistics             |
| engineering                          |
| sociology                            |
| archaeology                          |
| chemistry                            |
| geography                            |
| management                           |
| education                            |
| general science                      |
| elt - dictionaries_ reference        |
| humanities                           |
Task: Error-Detect (source dataset: Spreadsheet-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:
| ns1:numerwz       |
|:------------------|
| 17-wz/0001(admin) |
| 17-wz/0002(admin) |
| 17-wz/0003(admin) |
| 17-wz/0004(admin) |
| 17-wz/0006(admin) |
| 17-wz/0007(admin) |
| 17-wz/0008(admin) |
Task: Error-Detect (source dataset: Spreadsheet-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:
| last name   |
|:------------|
| loya        |
| goebbels    |
| defoe       |
| roy         |
Task: Error-Detect (source dataset: Spreadsheet-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:
| категория оборудования            |
|:----------------------------------|
| комплектующие                     |
| обжарочные сковороды              |
| грили_ духовки и свч-печи         |
| мини-пекарни                      |
| морозильные камеры и холодильники |
| промышленное оборудование         |
| bar equipment                     |
Task: Error-Detect (source dataset: Spreadsheet-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:
| u                                               |
|:------------------------------------------------|
| infoscale foundation                            |
| storage foundation enterprise ha                |
| infoscale enterprise                            |
| infoscale storage                               |
| storage foundation for oracle rac enterprise ha |
| volume replicator                               |
Task: Error-Detect (source dataset: Spreadsheet-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:
| agency                                        |
|:----------------------------------------------|
| export credit guarantee department            |
| competition and markets authority             |
| advisory conciliation and arbitration service |
| companies house                               |
| skills funding agency                         |
| uk intellectual property office               |
| ordnance survey                               |
| insolvency service                            |
Task: Error-Detect (source dataset: Spreadsheet-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:
| b    |
|:-----|
| int1 |
| int5 |
| intb |
| int2 |
Task: Error-Detect (source dataset: Spreadsheet-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:
| komórka sporządzająca dokument   |
|:---------------------------------|
| wzs-iv                           |
| wwrpo                            |
| wup                              |
| wfośigw                          |
| wzs                              |
| wzs-ii                           |
| ssom                             |
| kkbof                            |
| wzs-v                            |
| wzs-i                            |
| wzs-vi                           |
| wwśrpo                           |
Task: Error-Detect (source dataset: Spreadsheet-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:
| top hashtags in tweet in g2   |
|:------------------------------|
| happybirthday                 |
| classicrock                   |
| johnpauljones                 |
| laguns                        |
| sexaction                     |