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 |
|:-------------------------------------------------------------|:---------------------------------------|:------------------|-------:|
| implementation of magic-sets in a relational database system | inderpal singh mumick , hamid pirahesh | sigmod conference |   1994 |

Entity B:
| title                                                                             | authors                                                                                                                                     | venue             |   year |
|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| a general technique for querying xml documents using a relational database system | jayavel shanmugasundaram , eugene shekita , jerry kiernan , rajasekar krishnamurthy , efstratios viglas , jeffrey naughton , igor tatarinov | acm sigmod record |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 341
rtable_id: 569
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 |
|:------------------------------------|:---------------------|:--------------|-------:|
| reminiscences on influential papers | richard t. snodgrass | sigmod record |   1999 |

Entity B:
| title                               | authors         | venue             |   year |
|:------------------------------------|:----------------|:------------------|-------:|
| reminiscences on influential papers | kenneth a. ross | acm sigmod record |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 907
rtable_id: 649
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 |
|:--------------------------------------------------|:---------------------------------------------------------------------------------------------|:------------------|-------:|
| java and relational databases : sqlj ( tutorial ) | johannes klein , richard pledereder , gray clossman , brian becker , phil shaw , mark hapner | sigmod conference |   1998 |

Entity B:
| title                                                        | authors                          | venue                 |   year |
|:-------------------------------------------------------------|:---------------------------------|:----------------------|-------:|
| storage and retrieval of xml data using relational databases | surajit chaudhuri , kyuseok shim | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 133
rtable_id: 1413
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 |
|:-------------------------------------------|:------------------------------|:--------------|-------:|
| asserting beliefs in mls relational models | nenad jukic , susan v. vrbsky | sigmod record |   1997 |

Entity B:
| title                                      | authors                          | venue             |   year |
|:-------------------------------------------|:---------------------------------|:------------------|-------:|
| asserting beliefs in mls relational models | nenad a. jukic , susan v. vrbsky | acm sigmod record |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1750
rtable_id: 1757
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 |
|:------------------------------------------------------------|:-----------------------------------------------------------------------------|:--------------|-------:|
| lore : a database management system for semistructured data | roy goldman , serge abiteboul , jennifer widom , dallan quass , jason mchugh | sigmod record |   1997 |

Entity B:
| title                                                                                            | authors                     | venue                                         |   year |
|:-------------------------------------------------------------------------------------------------|:----------------------------|:----------------------------------------------|-------:|
| semantics for update rule programs and implementation in a relational database management system | louiqa raschid , jorge lobo | acm transactions on database systems ( tods ) |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2224
rtable_id: 1881
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 |
|:-----------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| aurora : a data stream management system | d. abadi , d. carney , u. &#199; etintemel , m. cherniack , c. convey , c. erwin , e. galvez , m. hatoun , a. maskey , a. rasin , a. singer , m. stonebraker , n. tatbul , y. xing , r. yan , s. zdonik | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 508
rtable_id: 2010
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 challenge of process data warehousing | matthias jarke , jörg köller , thomas list | vldb    |   2000 |

Entity B:
| title                                                              | authors                            | venue                                          |   year |
|:-------------------------------------------------------------------|:-----------------------------------|:-----------------------------------------------|-------:|
| the ins and outs ( and everything in between ) of data warehousing | phil fernandez , donovan schneider | international conference on management of data |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2358
rtable_id: 251
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 |
|:----------------------------------------------------|:------------------------------------------|:--------|-------:|
| approximate query processing : taming the terabytes | minos n. garofalakis , phillip b. gibbons | vldb    |   2001 |

Entity B:
| title                     | authors                                             | venue                                          |   year |
|:--------------------------|:----------------------------------------------------|:-----------------------------------------------|-------:|
| sequence query processing | praveen seshadri , miron livny , raghu ramakrishnan | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 713
rtable_id: 1640
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 |
|:-----------------------------------------------------------------------|:----------------------------------------------------------------|:------------------|-------:|
| dimensionality reduction for similarity searching in dynamic databases | ambuj k. singh , kothuri venkata ravi kanth , divyakant agrawal | sigmod conference |   1998 |

Entity B:
| title                         | authors                                                                               | venue                 |   year |
|:------------------------------|:--------------------------------------------------------------------------------------|:----------------------|-------:|
| proximity search in databases | roy goldman , narayanan shivakumar , suresh venkatasubramanian , hector garcia-molina | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 215
rtable_id: 780
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 |
|:--------------------------------------------------|:--------------|:------------------|-------:|
| paradise : a database system for gis applications | paradise team | sigmod conference |   1995 |

Entity B:
| title                                            | authors                             | venue                                          |   year |
|:-------------------------------------------------|:------------------------------------|:-----------------------------------------------|-------:|
| visdb : a system for visualizing large databases | daniel a. keim , hans-peter kriegel | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 306
rtable_id: 1527
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 |
|:------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| timber : a native system for querying xml | laks v. s. lakshmanan , stelios paparizos , nuwee wiwatwattana , cong yu , jignesh m. patel , h. v. jagadish , andrew nierman , divesh srivastava , adriane chapman , yuqing wu , shurug al-khalifa | sigmod conference |   2003 |

Entity B:
| title                                     | authors                                                                                                                                                                                             | venue                                          |   year |
|:------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| timber : a native system for querying xml | stelios paparizos , shurug al-khalifa , adriane chapman , h. v. jagadish , laks v. s. lakshmanan , andrew nierman , jignesh m. patel , divesh srivastava , nuwee wiwatwattana , yuqing wu , cong yu | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1849
rtable_id: 2017
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 |
|:-------------------------------------------------------------------------------|:----------------|:----------------------------|-------:|
| ensuring consistency in multidatabases by preserving two-level serializability | sharad mehrotra | acm trans . database syst . |   1998 |

Entity B:
| title                                                                          | authors                                                                  | venue                                         |   year |
|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------------|:----------------------------------------------|-------:|
| ensuring consistency in multidatabases by preserving two-level serializability | sharad mehrotra , rajeev rastogi , henry f. korth , abraham silberschatz | acm transactions on database systems ( tods ) |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 969
rtable_id: 133
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 |
|:-------------------|:------------|:--------------|-------:|
| book review column | karl aberer | sigmod record |   2003 |

Entity B:
| title              | authors     | venue             |   year |
|:-------------------|:------------|:------------------|-------:|
| book review column | karl aberer | acm sigmod record |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1380
rtable_id: 2202
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 |
|:-----------------------------------------------------------------|:-----------------------------------------------------|:--------|-------:|
| an adaptive hybrid server architecture for client caching odbmss | m. tamer özsu , kaladhar voruganti , ronald c. unrau | vldb    |   1999 |

Entity B:
| title                                                    | authors                             | venue                                          |   year |
|:---------------------------------------------------------|:------------------------------------|:-----------------------------------------------|-------:|
| upsizing form file server to client server architectures | corporate the access team microsoft | international conference on management of data |   1995 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2420
rtable_id: 1496
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 placement in shared-nothing parallel database systems | manish mehta , david j. dewitt | vldb j. |   1997 |

Entity B:
| title                                                         | authors                                            | venue                                                                  |   year |
|:--------------------------------------------------------------|:---------------------------------------------------|:-----------------------------------------------------------------------|-------:|
| data partitioning and load balancing in parallel disk systems | peter scheuermann , gerhard weikum , peter zabback | the vldb journal -- the international journal on very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 545
rtable_id: 1250
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 |
|:--------------------------------------------------------------------------------|:----------------------------|:------------------|-------:|
| semint : a system prototype for semantic integration in heterogeneous databases | wen-syan li , chris clifton | sigmod conference |   1995 |

Entity B:
| title                                                      | authors      | venue                 |   year |
|:-----------------------------------------------------------|:-------------|:----------------------|-------:|
| lopix : a system for xml data integration and manipulation | wolfgang may | very large data bases |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 135
rtable_id: 1415
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 |
|:------------------------|:----------------------------------------------------|:--------------|-------:|
| xml document versioning | carlo zaniolo , vassilis j. tsotras , shu-yao chien | sigmod record |   2001 |

Entity B:
| title                   | authors                                             | venue             |   year |
|:------------------------|:----------------------------------------------------|:------------------|-------:|
| xml document versioning | shu yao chien , vassilis j. tsotras , carlo zaniolo | acm sigmod record |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1739
rtable_id: 573
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 microsoft database research group | roger s. barga , david b. lomet , per-åke larson , surajit chaudhuri , vivek r. narasayya | sigmod record |   1998 |

Entity B:
| title                                 | authors                                                                       | venue             |   year |
|:--------------------------------------|:------------------------------------------------------------------------------|:------------------|-------:|
| the microsoft database research group | david lomet , roger barga , surajit chaudhuri , paul larson , vivek narasayya | acm sigmod record |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2477
rtable_id: 228
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 |
|:-----------------------------------------|:------------------|:--------------|-------:|
| temporal database system implementations | michael h. böhlen | sigmod record |   1995 |

Entity B:
| title                        | authors                                                                   | venue                                         |   year |
|:-----------------------------|:--------------------------------------------------------------------------|:----------------------------------------------|-------:|
| temporal statement modifiers | michael h. b &#246; hlen , christian s. jensen , richard thomas snodgrass | acm transactions on database systems ( tods ) |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 162
rtable_id: 369
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 onion technique : indexing for linear optimization queries | chung-sheng li , vittorio castelli , lawrence d. bergman , john r. smith , yuan-chi chang , ming-ling lo | sigmod conference |   2000 |

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: 526
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 |
|:-------------------------|:------------------------------------------------------------------------------|:--------|-------:|
| the microsoft repository | david shutt , jason zander , brian harry , paul sanders , philip a. bernstein | vldb    |   1997 |

Entity B:
| title                                 | authors                                                                       | venue             |   year |
|:--------------------------------------|:------------------------------------------------------------------------------|:------------------|-------:|
| the microsoft database research group | david lomet , roger barga , surajit chaudhuri , paul larson , vivek narasayya | acm sigmod record |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2159
rtable_id: 228
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 |
|:----------------------------------------------------|:--------------------------------------------------------|:------------------|-------:|
| hd-eye : visual clustering of high dimensional data | daniel a. keim , markus wawryniuk , alexander hinneburg | sigmod conference |   2002 |

Entity B:
| title                                                          | authors                                                                                          | venue                                          |   year |
|:---------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| multi-dimensional clustering : a new data layout scheme in db2 | sriram padmanabhan , bishwaranjan bhattacharjee , tim malkemus , leslie cranston , matthew huras | international conference on management of data |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2354
rtable_id: 2007
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 |
|:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------|-------:|
| proxy-based acceleration of dynamically generated content on the world wide web : an approach and implementation | suresha , kaushik dutta , krithi ramamritham , anindya datta , helen m. thomas , debra e. vandermeer | sigmod conference |   2002 |

Entity B:
| title                                                                                                            | authors                                                                                        | venue                                          |   year |
|:-----------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| proxy-based acceleration of dynamically generated content on the world wide web : an approach and implementation | anindya datta , kaushik dutta , helen thomas , debra vandermeer , suresha , krithi ramamritham | international conference on management of data |   2002 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1253
rtable_id: 696
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 |
|:-----------------------------|:-----------------------------------|:------------------|-------:|
| data grid management systems | arun jagatheesan , arcot rajasekar | sigmod conference |   2003 |

Entity B:
| title                                                                       | authors                                                               | venue                                          |   year |
|:----------------------------------------------------------------------------|:----------------------------------------------------------------------|:-----------------------------------------------|-------:|
| on supporting containment queries in relational database management systems | chun zhang , jeffrey naughton , david dewitt , qiong luo , guy lohman | international conference on management of data |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 393
rtable_id: 262
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 exploration of large scientific databases | gustavo alonso , etzard stolte | vldb    |   2002 |

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: 2409
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 |
|:-----------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------|-------:|
| metu interoperable database system | ebru kilic , pinar koksal , cem evrendilek , ugur halici , sema mancuhan , n. kesim , asuman dogac , cevdet dengi , sena nural , gökhan özhan , fatma ozcan , ismailcem budak arpinar | sigmod record |   1995 |

Entity B:
| title                                   | authors                                                                           | venue                                          |   year |
|:----------------------------------------|:----------------------------------------------------------------------------------|:-----------------------------------------------|-------:|
| the mlpq/gis constraint database system | peter revesz , rui chen , pradip kanjamala , yiming li , yuguo liu , yonghui wang | international conference on management of data |   2000 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2602
rtable_id: 488
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 |
|:-----------------------------------------------------------------|:-----------------------------------------------------|:--------|-------:|
| an adaptive hybrid server architecture for client caching odbmss | m. tamer özsu , kaladhar voruganti , ronald c. unrau | vldb    |   1999 |

Entity B:
| title                                                            | authors                                                    | venue                 |   year |
|:-----------------------------------------------------------------|:-----------------------------------------------------------|:----------------------|-------:|
| an adaptive hybrid server architecture for client caching odbmss | kaladhar voruganti , m. tamer &#214; zsu , ronald c. unrau | very large data bases |   1999 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2420
rtable_id: 797
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 |
|:---------------------------------------------------------------------------------------------------------|:------------------------------|:------------------|-------:|
| mosaico - a system for conceptual modeling and rapid prototyping of object-oriented database application | m. toiati , michele missikoff | sigmod conference |   1994 |

Entity B:
| title                                                                                                  | authors                  | venue                                          |   year |
|:-------------------------------------------------------------------------------------------------------|:-------------------------|:-----------------------------------------------|-------:|
| mosaico-a system for conceptual modeling and rapid prototyping of object-oriented database application | m. missikoff , m. toiati | international conference on management of data |   1994 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 980
rtable_id: 1664
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 |
|:-------------------------------------|:--------------------------------------|:--------|-------:|
| mining generalized association rules | ramakrishnan srikant , rakesh agrawal | vldb    |   1995 |

Entity B:
| title                                                         | authors                                                   | venue                 |   year |
|:--------------------------------------------------------------|:----------------------------------------------------------|:----------------------|-------:|
| on the discovery of interesting patterns in association rules | sridhar ramaswamy , sameer mahajan , abraham silberschatz | very large data bases |   1998 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2246
rtable_id: 984
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 |
|:--------------------------------------------------------------|:-------------------------------------------|:--------------|-------:|
| relational data sharing in peer-based data management systems | beng chin ooi , yanfeng shu , kian-lee tan | sigmod record |   2003 |

Entity B:
| title                                                      | authors                        | venue                                                                  |   year |
|:-----------------------------------------------------------|:-------------------------------|:-----------------------------------------------------------------------|-------:|
| data placement in shared-nothing parallel database systems | manish mehta , david j. dewitt | the vldb journal -- the international journal on very large data bases |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2402
rtable_id: 1160
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 nd-tree : a dynamic indexing technique for multidimensional non-ordered discrete data spaces | gang qian , qiang xue , sakti pramanik , qiang zhu | vldb    |   2003 |

Entity B:
| title                                                     | authors                                                | venue                 |   year |
|:----------------------------------------------------------|:-------------------------------------------------------|:----------------------|-------:|
| the x-tree : an index structure for high-dimensional data | stefan berchtold , daniel a. keim , hans-peter kriegel | very large data bases |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 203
rtable_id: 1044
label: 0