MMTU-benchmark/MMTU huggingface dataset:
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                               | authors         | venue   |   year |
|:----------------------------------------------------|:----------------|:--------|-------:|
| searching in metric spaces by spatial approximation | gonzalo navarro | vldb j. |   2002 |

Entity B:
| title                                                  | authors                                 | venue                 |   year |
|:-------------------------------------------------------|:----------------------------------------|:----------------------|-------:|
| a raster approximation for processing of spatial joins | geraldo zimbrao , jano moreira de souza | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 467
rtable_id: 992
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                                  | authors                      | venue   |   year |
|:-------------------------------------------------------------------------------------------------------|:-----------------------------|:--------|-------:|
| optimization algorithms for exploiting the parallelism-communication tradeoff in pipelined parallelism | waqar hasan , rajeev motwani | vldb    |   1994 |

Entity B:
| title                                                      | authors                      | venue                 |   year |
|:-----------------------------------------------------------|:-----------------------------|:----------------------|-------:|
| coloring away communication in parallel query optimization | waqar hasan , rajeev motwani | very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2056
rtable_id: 1442
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                          | authors                                                                                              | venue             |   year |
|:---------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------|-------:|
| multi-dimensional clustering : a new data layout scheme in db2 | bishwaranjan bhattacharjee , sriram padmanabhan , matthew huras , leslie cranston , timothy malkemus | sigmod conference |   2003 |

Entity B:
| title                                                        | authors                                                         | venue                 |   year |
|:-------------------------------------------------------------|:----------------------------------------------------------------|:----------------------|-------:|
| mining multi-dimensional constrained gradients in data cubes | guozhu dong , jiawei han , joyce m. w. lam , jian pei , ke wang | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2333
rtable_id: 1280
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                               | authors                                                                                                | venue         |   year |
|:--------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|:--------------|-------:|
| database research at the university of illinois at urbana-champaign | chengxiang zhai , marianne winslett , yuanyuan zhou , kevin chen-chuan chang , jiawei han , anhai doan | sigmod record |   2002 |

Entity B:
| title                              | authors          | venue             |   year |
|:-----------------------------------|:-----------------|:------------------|-------:|
| database research at nthu and itri | arbee l. p. chen | acm sigmod record |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 826
rtable_id: 1689
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                            | authors                             | venue             |   year |
|:-------------------------------------------------|:------------------------------------|:------------------|-------:|
| visdb : a system for visualizing large databases | hans-peter kriegel , daniel a. keim | sigmod conference |   1995 |

Entity B:
| title                                                                | authors   | venue                 |   year |
|:---------------------------------------------------------------------|:----------|:----------------------|-------:|
| mineset ( tm ) : a system for high-end data mining and visualization |           | very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2270
rtable_id: 1146
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                         | authors                                                                               | venue   |   year |
|:------------------------------|:--------------------------------------------------------------------------------------|:--------|-------:|
| proximity search in databases | narayanan shivakumar , hector garcia-molina , roy goldman , suresh venkatasubramanian | vldb    |   1998 |

Entity B:
| title                                                              | authors        | venue                                          |   year |
|:-------------------------------------------------------------------|:---------------|:-----------------------------------------------|-------:|
| efficient geometry-based similarity search of 3d spatial databases | daniel a. keim | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1138
rtable_id: 49
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                      | authors                       | venue   |   year |
|:---------------------------|:------------------------------|:--------|-------:|
| query optimization for xml | jason mchugh , jennifer widom | vldb    |   1999 |

Entity B:
| title                                                              | authors                                                      | venue                                          |   year |
|:-------------------------------------------------------------------|:-------------------------------------------------------------|:-----------------------------------------------|-------:|
| a query language and optimization techniques for unstructured data | peter buneman , susan davidson , gerd hillebrand , dan suciu | international conference on management of data |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1664
rtable_id: 1992
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                      | authors                                                    | venue   |   year |
|:-------------------------------------------|:-----------------------------------------------------------|:--------|-------:|
| composing web services on the semantic web | athman bouguettaya , brahim medjahed , ahmed k. elmagarmid | vldb j. |   2003 |

Entity B:
| title                                         | authors                       | venue             |   year |
|:----------------------------------------------|:------------------------------|:------------------|-------:|
| the grid : an application of the semantic web | carole goble , david de roure | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 259
rtable_id: 618
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                         | authors                                                                          | venue             |   year |
|:----------------------------------------------|:---------------------------------------------------------------------------------|:------------------|-------:|
| the multidimensional database system rasdaman | andreas dehmel , peter baumann , norbert widmann , roland ritsch , paula furtado | sigmod conference |   1998 |

Entity B:
| title                                                     | authors        | venue             |   year |
|:----------------------------------------------------------|:---------------|:------------------|-------:|
| olap , relational , and multidimensional database systems | george colliat | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 58
rtable_id: 1884
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                     | authors                    | venue             |   year |
|:--------------------------------------------------------------------------|:---------------------------|:------------------|-------:|
| aries/csa : a method for database recovery in client-server architectures | c. mohan , inderpal narang | sigmod conference |   1994 |

Entity B:
| title                                                                     | authors                    | venue                                          |   year |
|:--------------------------------------------------------------------------|:---------------------------|:-----------------------------------------------|-------:|
| aries/csa : a method for database recovery in client-server architectures | c. mohan , inderpal narang | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 300
rtable_id: 1742
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                 | authors                                                                                  | venue   |   year |
|:------------------------------------------------------|:-----------------------------------------------------------------------------------------|:--------|-------:|
| integrating the ub-tree into a database system kernel | robert fenk , rudolf bayer , volker markl , martin zirkel , frank ramsak , klaus elhardt | vldb    |   2000 |

Entity B:
| title                                                                                                         | authors                  | venue                                                                  |   year |
|:--------------------------------------------------------------------------------------------------------------|:-------------------------|:-----------------------------------------------------------------------|-------:|
| integrating symbolic images into a multimedia database system using classification and abstraction approaches | aya soffer , hanan samet | the vldb journal -- the international journal on very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2523
rtable_id: 1171
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                    | authors                  | venue         |   year |
|:-----------------------------------------|:-------------------------|:--------------|-------:|
| data management for earth system science | james frew , jeff dozier | sigmod record |   1997 |

Entity B:
| title                                   | authors            | venue                                          |   year |
|:----------------------------------------|:-------------------|:-----------------------------------------------|-------:|
| database systems management and oracle8 | c. gregory doherty | international conference on management of data |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1346
rtable_id: 171
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                      | authors        | venue         |   year |
|:-------------------------------------------------------------------------------------------|:---------------|:--------------|-------:|
| research issues in active database systems : report from the closing panel at ride-ads '94 | jennifer widom | sigmod record |   1994 |

Entity B:
| title                                           | authors        | venue             |   year |
|:------------------------------------------------|:---------------|:------------------|-------:|
| advances in real-time database systems research | azer bestavros | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 770
rtable_id: 372
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                         | authors                                                    | venue   |   year |
|:----------------------------------------------|:-----------------------------------------------------------|:--------|-------:|
| efficient search of multi-dimensional b-trees | dave birdsall , hedieh yaghmai , harry leslie , rohit jain | vldb    |   1995 |

Entity B:
| title                                        | authors                              | venue                 |   year |
|:---------------------------------------------|:-------------------------------------|:----------------------|-------:|
| a foundation for multi-dimensional databases | marc gyssens , laks v. s. lakshmanan | very large data bases |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1263
rtable_id: 1026
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                         | authors                                                    | venue   |   year |
|:----------------------------------------------|:-----------------------------------------------------------|:--------|-------:|
| efficient search of multi-dimensional b-trees | dave birdsall , hedieh yaghmai , harry leslie , rohit jain | vldb    |   1995 |

Entity B:
| title                                         | authors                                                    | venue                 |   year |
|:----------------------------------------------|:-----------------------------------------------------------|:----------------------|-------:|
| efficient search of multi-dimensional b-trees | harry leslie , rohit jain , dave birdsall , hedieh yaghmai | very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1263
rtable_id: 1112
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                     | authors                                       | venue   |   year |
|:----------------------------------------------------------|:----------------------------------------------|:--------|-------:|
| fast high-dimensional data search in incomplete databases | kian-lee tan , cheng hian goh , beng chin ooi | vldb    |   1998 |

Entity B:
| title                                                | authors      | venue                 |   year |
|:-----------------------------------------------------|:-------------|:----------------------|-------:|
| obtaining complete answers from incomplete databases | alon y. levy | very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 630
rtable_id: 1102
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                     | authors                                              | venue   |   year |
|:----------------------------------------------------------|:-----------------------------------------------------|:--------|-------:|
| materialized view selection for multidimensional datasets | jeffrey f. naughton , amit shukla , prasad deshpande | vldb    |   1998 |

Entity B:
| title                                         | authors                                                      | venue                                          |   year |
|:----------------------------------------------|:-------------------------------------------------------------|:-----------------------------------------------|-------:|
| the multidimensional database system rasdaman | p. baumann , a. dehmel , p. furtado , r. ritsch , n. widmann | international conference on management of data |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 829
rtable_id: 147
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                      | authors                                                         | venue         |   year |
|:-----------------------------------------------------------|:----------------------------------------------------------------|:--------------|-------:|
| the implementation and performance of compressed databases | till westmann , sven helmer , guido moerkotte , donald kossmann | sigmod record |   2000 |

Entity B:
| title                                                                  | authors                           | venue             |   year |
|:-----------------------------------------------------------------------|:----------------------------------|:------------------|-------:|
| performing joins without decompression in a compressed database system | s. j. o'connell , n. winterbottom | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1644
rtable_id: 900
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                          | authors                                                                                                                 | venue   |   year |
|:-----------------------------------------------|:------------------------------------------------------------------------------------------------------------------------|:--------|-------:|
| anatomy of a native xml base management system | thorsten fiebig , till westmann , robert schiele , guido moerkotte , carl-christian kanne , julia neumann , sven helmer | vldb j. |   2002 |

Entity B:
| title                                   | authors            | venue                                          |   year |
|:----------------------------------------|:-------------------|:-----------------------------------------------|-------:|
| database systems management and oracle8 | c. gregory doherty | international conference on management of data |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 508
rtable_id: 171
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                | authors                                       | venue             |   year |
|:---------------------------------------------------------------------|:----------------------------------------------|:------------------|-------:|
| birch : an efficient data clustering method for very large databases | raghu ramakrishnan , miron livny , tian zhang | sigmod conference |   1996 |

Entity B:
| title                                                                  | authors                                                   | venue                 |   year |
|:-----------------------------------------------------------------------|:----------------------------------------------------------|:----------------------|-------:|
| an efficient algorithm for mining association rules in large databases | ashoka savasere , edward omiecinski , shamkant b. navathe | very large data bases |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 144
rtable_id: 1471
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                        | authors                          | venue             |   year |
|:-------------------------------------------------------------|:---------------------------------|:------------------|-------:|
| tam : a system for dynamic transactional activity management | ling liu , calton pu , tong zhou | sigmod conference |   1999 |

Entity B:
| title                                                          | authors                            | venue                                          |   year |
|:---------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| dynamat : a dynamic view management system for data warehouses | yannis kotidis , nick roussopoulos | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1028
rtable_id: 53
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                       | authors                                    | venue             |   year |
|:--------------------------------------------|:-------------------------------------------|:------------------|-------:|
| partial results for online query processing | joseph m. hellerstein , vijayshankar raman | sigmod conference |   2002 |

Entity B:
| title                                | authors                               | venue                                          |   year |
|:-------------------------------------|:--------------------------------------|:-----------------------------------------------|-------:|
| online query processing : a tutorial | peter j. haas , joseph m. hellerstein | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1690
rtable_id: 365
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                  | authors         | venue   |   year |
|:---------------------------------------------------------------------------------------|:----------------|:--------|-------:|
| panel : future directions of database research - the vldb broadening strategy , part 1 | hans-jörg schek | vldb    |   2000 |

Entity B:
| title                                                                                  | authors                | venue                 |   year |
|:---------------------------------------------------------------------------------------|:-----------------------|:----------------------|-------:|
| panel : future directions of database research - the vldb broadening strategy , part 1 | hans-j &#246; rg schek | very large data bases |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1007
rtable_id: 1370
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                                                      | authors       | venue   |   year |
|:-------------------------------------------------------------------------------------------|:--------------|:--------|-------:|
| the persistent cache : improving oid indexing in temporal object-oriented database systems | kjetil nørvåg | vldb    |   1999 |

Entity B:
| title                                                           | authors   | venue                                          |   year |
|:----------------------------------------------------------------|:----------|:-----------------------------------------------|-------:|
| unisql/x unified relational and object-oriented database system | won kim   | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 118
rtable_id: 1630
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                           | authors                                                                          | venue             |   year |
|:--------------------------------|:---------------------------------------------------------------------------------|:------------------|-------:|
| exchanging intensional xml data | omar benjelloun , tova milo , serge abiteboul , frederic dang ngoc , bernd amann | sigmod conference |   2003 |

Entity B:
| title                           | authors                                                                      | venue                                          |   year |
|:--------------------------------|:-----------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| exchanging intensional xml data | tova milo , serge abiteboul , bernd amann , omar benjelloun , fred dang ngoc | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1783
rtable_id: 2035
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                             | authors       | venue             |   year |
|:--------------------------------------------------|:--------------|:------------------|-------:|
| paradise : a database system for gis applications | paradise team | sigmod conference |   1995 |

Entity B:
| title                                             | authors                     | venue                                          |   year |
|:--------------------------------------------------|:----------------------------|:-----------------------------------------------|-------:|
| paradise : a database system for gis applications | corporate the paradise team | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 306
rtable_id: 1522
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                              | authors                                                   | venue   |   year |
|:---------------------------------------------------|:----------------------------------------------------------|:--------|-------:|
| a performance evaluation of oid mapping techniques | andré eickler , carsten andreas gerlhof , donald kossmann | vldb    |   1995 |

Entity B:
| title                                            | authors                                  | venue                 |   year |
|:-------------------------------------------------|:-----------------------------------------|:----------------------|-------:|
| an evaluation of generic bulk loading techniques | jochen van den bercken , bernhard seeger | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1430
rtable_id: 1304
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                     | authors                                                  | venue         |   year |
|:----------------------------------------------------------|:---------------------------------------------------------|:--------------|-------:|
| improving timeliness in real-time secure database systems | sang hyuk son , rasikan david , bhavani m. thuraisingham | sigmod record |   1996 |

Entity B:
| title                                                   | authors                   | venue             |   year |
|:--------------------------------------------------------|:--------------------------|:------------------|-------:|
| an annotated bibliography on real-time database systems | &#214; zg &#252; r ulusoy | acm sigmod record |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 925
rtable_id: 1614
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                     | authors       | venue         |   year |
|:------------------------------------------|:--------------|:--------------|-------:|
| database principles column - introduction | leonid libkin | sigmod record |   1999 |

Entity B:
| title               | authors   | venue             |   year |
|:--------------------|:----------|:------------------|-------:|
| database principles |           | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2144
rtable_id: 2128
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                      | authors             | venue         |   year |
|:-------------------------------------------|:--------------------|:--------------|-------:|
| repositories and object oriented databases | philip a. bernstein | sigmod record |   1998 |

Entity B:
| title                                      | authors             | venue             |   year |
|:-------------------------------------------|:--------------------|:------------------|-------:|
| repositories and object oriented databases | philip a. bernstein | acm sigmod record |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 692
rtable_id: 1761
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                          | authors         | venue   |   year |
|:-------------------------------|:----------------|:--------|-------:|
| the adabas buffer pool manager | harald schöning | vldb    |   1998 |

Entity B:
| title                          | authors                | venue                 |   year |
|:-------------------------------|:-----------------------|:----------------------|-------:|
| the adabas buffer pool manager | harald sch &#246; ning | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 625
rtable_id: 978
label: 1
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                       | authors        | venue             |   year |
|:----------------------------|:---------------|:------------------|-------:|
| databases and visualization | daniel a. keim | sigmod conference |   1996 |

Entity B:
| title                             | authors             | venue             |   year |
|:----------------------------------|:--------------------|:------------------|-------:|
| dynamic information visualization | yannis e. ioannidis | acm sigmod record |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1866
rtable_id: 1888
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                                                            | authors     | venue             |   year |
|:-----------------------------------------------------------------|:------------|:------------------|-------:|
| data management in ecommerce : the good , the bad , and the ugly | avigdor gal | sigmod conference |   2000 |

Entity B:
| title                                         | authors             | venue                                          |   year |
|:----------------------------------------------|:--------------------|:-----------------------------------------------|-------:|
| data management issues in electronic commerce | m. tamer &#214; zsu | international conference on management of data |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2113
rtable_id: 56
label: 0
Task: Entity-Matching (source dataset: DBLP-ACM)

You are given two data records from two datasets that may refer to the same real-world entity. Each record consists of multiple attributes, such as name, address, and other identifying details. Your goal is to determine whether pairs of records refer to the same entity (match) or different entities (non-match).

No explanation and return the final answer as a JSON object, {"label": "<LABEL>"}, where "<LABEL>" is either "match" or "non-match".

Entity A:
| title                             | authors                                                   | venue         |   year |
|:----------------------------------|:----------------------------------------------------------|:--------------|-------:|
| cluster validity methods : part i | michalis vazirgiannis , yannis batistakis , maria halkidi | sigmod record |   2002 |

Entity B:
| title                                          | authors                                                   | venue             |   year |
|:-----------------------------------------------|:----------------------------------------------------------|:------------------|-------:|
| clustering validity checking methods : part ii | maria halkidi , yannis batistakis , michalis vazirgiannis | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 188
rtable_id: 514
label: 0