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

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 query language for multidimensional arrays : design , implementation , and optimization techniques | l libkin , r machlin , l wong | sigmod conference |   1996 |

Entity B:
| title                                                              | authors             | venue                                                                 | year   |
|:-------------------------------------------------------------------|:--------------------|:----------------------------------------------------------------------|:-------|
| aquery : query language for ordered data , optimization techniques | a lerner , d shasha | and experiments . technical report , march 2003 . csdocs . cs . nyu . |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1466
rtable_id: 30493
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 sdss skyserver : public access to the sloan digital sky server data | a szalay , j gray , a thakar , p kunszt , t malik , j raddick , c stoughton , j vandenberg | sigmod conference |   2002 |

Entity B:
| title                                                                     | authors                                                | venue   | year   |
|:--------------------------------------------------------------------------|:-------------------------------------------------------|:--------|:-------|
| the sdss skyserverâ ?? public access to the sloan digital sky server data | j gray , a szalay , a thakar , pz zunszt , t malik , j |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 556
rtable_id: 35137
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 | w li , c clifton | sigmod conference |   1995 |

Entity B:
| title                                                                                        | authors                                         | venue           |   year |
|:---------------------------------------------------------------------------------------------|:------------------------------------------------|:----------------|-------:|
| report of the workshop on semantic heterogeneity and interoperation in multidatabase systems | p drew , r king , d mcleod , m rusinkiewicz , a | sigmod record , |   1993 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1694
rtable_id: 41168
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 numerical error bounding for replicated network services | h yu , a vahdat | vldb    |   2000 |

Entity B:
| title                                                              | authors       | venue                                                            | year   |
|:-------------------------------------------------------------------|:--------------|:-----------------------------------------------------------------|:-------|
| efficient numerical error bounding for replicated network services | hy aminvahdat | technical report cs-2000-08 , computer science department , duke |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1199
rtable_id: 102
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:------------------------------------------------------------------------------------------|:-------------------------|:----------------------------|-------:|
| information gathering in the world-wide web : the w3ql query language and the w3qs system | d konopnicki , o shmueli | acm trans . database syst . |   1998 |

Entity B:
| title                                                                             | authors     | venue                        | year   |
|:----------------------------------------------------------------------------------|:------------|:-----------------------------|:-------|
| information gathering in the world wide web : the w3ql language , the w3qs system | dko shmueli | acm transactions on database |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2360
rtable_id: 58868
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 mining with relational database systems : alternatives and implications | s sarawagi , s thomas , r agrawal | sigmod conference |   1998 |

Entity B:
| title                                                                        | authors                           | venue                   | year   |
|:-----------------------------------------------------------------------------|:----------------------------------|:------------------------|:-------|
| integrating mining with relational databases : alternatives and implications | s sarawagi , s thomas , r agrawal | acm sigmod conference , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1180
rtable_id: 10641
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 | j mchugh , j widom | vldb    |   1999 |

Entity B:
| title                                                | authors                     | venue   | year   |
|:-----------------------------------------------------|:----------------------------|:--------|:-------|
| improving query optimization for disjunctive datalog | c cumbo , w faber , g greco |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1431
rtable_id: 23894
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:---------------------------------------------------------------------|:--------------------------------|:--------------|-------:|
| semistructured und structured data in the web : going back and forth | p atzeni , g mecca , p merialdo | sigmod record |   1997 |

Entity B:
| title                                                            | authors                           | venue           |   year |
|:-----------------------------------------------------------------|:----------------------------------|:----------------|-------:|
| data on the web : from relational to semistructured data and xml | s abiteboul , p buneman , d suciu | sigmod record , |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 514
rtable_id: 31560
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:------------------------------------------------------|:-----------------------|:--------------|-------:|
| constructing the next 100 database management systems | a geppert , k dittrich | sigmod record |   1994 |

Entity B:
| title                                                   | authors                              | venue   | year   |
|:--------------------------------------------------------|:-------------------------------------|:--------|:-------|
| self-managing technology in database management systems | s chaudhuri , b dageville , g lohman |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 996
rtable_id: 48864
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-----------------------------------------------------------------------------------------|:----------|:--------|-------:|
| object-relational database systems : principles , products , and challenges ( tutorial ) |           |         |   1997 |

Entity B:
| title                                                                               | authors              | venue         | year   |
|:------------------------------------------------------------------------------------|:---------------------|:--------------|:-------|
| managing financial time series data : object-relational and object database systems | l molesky , m caruso | tutorial vldb |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1380
rtable_id: 4575
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 multi-paradigm querying approach for a generic multimedia database management system | j wen , q li , w ma , h zhang | sigmod record |   2003 |

Entity B:
| title                                                                                        | authors             | venue                                                        | year   |
|:---------------------------------------------------------------------------------------------|:--------------------|:-------------------------------------------------------------|:-------|
| a heuristic approach to attribute partitioning in a self-adaptive database management system | m hammer , b niamir | proceedings of the international conference on management of |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2575
rtable_id: 11675
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 efficient algorithm for mining association rules in large databases | a savasere , e omiecinski , s navathe | vldb    |   1995 |

Entity B:
| title                                                             | authors                  | venue   | year   |
|:------------------------------------------------------------------|:-------------------------|:--------|:-------|
| mining association rules in entity-relationship modeled databases | l cristofor , d simovici |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1953
rtable_id: 57892
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 | r srikant , r agrawal | vldb    |   1995 |

Entity B:
| title                                                         | authors       | venue                                                              | year   |
|:--------------------------------------------------------------|:--------------|:-------------------------------------------------------------------|:-------|
| mining implication-based fuzzy association rules in databases | e hullermeier | intelligent systems for information processing : from & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 19
rtable_id: 64215
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-----------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|-------:|
| aurora : a data stream management system | d abadi , d carney , u чetintemel , 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 | sigmod conference |   2003 |

Entity B:
| title                                  | authors                                              | venue                                                                  |   year |
|:---------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------------------------|-------:|
| load shedding in a data stream manager | n tatbul , u cetintemel , s zdonik , m cherniack , m | proceedings of the international conference on very large & hellip ; , |   2003 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 280
rtable_id: 40797
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:---------------------------------------------------------------------|:---------------------------------|:--------|-------:|
| querying heterogeneous information sources using source descriptions | a levy , a rajaraman , j ordille | vldb    |   1996 |

Entity B:
| title                                                                                 | authors               | venue                                          | year   |
|:--------------------------------------------------------------------------------------|:----------------------|:-----------------------------------------------|:-------|
| j. . j. ordille . quering heterogeneous information sources using source descriptions | ay levi , a rajaraman | proc . 22nd vldb conference , bombay , india , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 2581
rtable_id: 11030
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:----------------------------------------------------|:----------------------------|:------------------|-------:|
| scalable parallel data mining for association rules | e han , g karypis , v kumar | sigmod conference |   1997 |

Entity B:
| title                          | authors          | venue   | year   |
|:-------------------------------|:-----------------|:--------|:-------|
| mining fuzzy association rules | kcc chan , wh au |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1288
rtable_id: 39932
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 databases for querying xml documents : limitations and opportunities | j shanmugasundaram , k tufte , c zhang , g he , d dewitt , j naughton | vldb    |   1999 |

Entity B:
| title                                                                                  | authors                                               | venue                               | year   |
|:---------------------------------------------------------------------------------------|:------------------------------------------------------|:------------------------------------|:-------|
| relational databases for querying xml documents : limitations and opportunities . intl | j shanmugasundaram , h gang , k tufte , c zhang , djd | conference on very large data bases |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1424
rtable_id: 51976
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 two semantic query optimization techniques in db2 universal database | q cheng , j gryz , f koo , t leung , l liu , x qian , k schiefer | vldb    |   1999 |

Entity B:
| title                                    | authors                            | venue                                                                    |   year |
|:-----------------------------------------|:-----------------------------------|:-------------------------------------------------------------------------|-------:|
| query sampling in db2 universal database | j gryz , j guo , l liu , c zuzarte | proceedings of the 2004 acm sigmod international conference & hellip ; , |   2004 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1616
rtable_id: 57689
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 architecture to support scalable online personalization on the web | a datta , k dutta , d vandermeer , k ramamritham , s navathe | vldb j. |   2001 |

Entity B:
| title                                              | authors                                        | venue                                                      | year   |
|:---------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------------------|:-------|
| an architecture for scalable on-chip communication | j liang , a laffely , s srinivasan , r tessier | technical report , university of massachusetts , amherst , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 2503
rtable_id: 46086
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------------------------|:--------------------------|:--------|-------:|
| querying multiple features of groups in relational databases | d chatziantoniou , k ross | vldb    |   1996 |

Entity B:
| title                                  | authors                   | venue   | year   |
|:---------------------------------------|:--------------------------|:--------|:-------|
| subset queries in relational databases | sr valluri , k karlapalem |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1986
rtable_id: 22336
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------|:----------------------------------------------------|:------------------|-------:|
| on-line reorganization in object databases | m lakhamraju , r rastogi , s seshadri , s sudarshan | sigmod conference |   2000 |

Entity B:
| title                                             | authors   | venue   | year   |
|:--------------------------------------------------|:----------|:--------|:-------|
| a method for on-line reorganization of a database | gh sockut |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 813
rtable_id: 42580
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 network is the database : data management for highly distributed systems | j navas , m wynblatt | sigmod conference |   2001 |

Entity B:
| title                                                                  | authors   | venue   | year   |
|:-----------------------------------------------------------------------|:----------|:--------|:-------|
| building an interoperable distributed image database management system | b yao     |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 707
rtable_id: 53791
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:--------------------------------------|:---------------------------------|:------------------|-------:|
| rights protection for relational data | r sion , m atallah , s prabhakar | sigmod conference |   2003 |

Entity B:
| title                                                         | authors                | venue   |   year |
|:--------------------------------------------------------------|:-----------------------|:--------|-------:|
| query execution and index selection for relational data bases | g farley , sa schuster | vldb ,  |   1975 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 268
rtable_id: 37511
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------|:---------------------------------------|:--------|-------:|
| adaptive data broadcast in hybrid networks | k stathatos , n roussopoulos , j baras | vldb    |   1997 |

Entity B:
| title                                      | authors                                 | venue                                                                  |   year |
|:-------------------------------------------|:----------------------------------------|:-----------------------------------------------------------------------|-------:|
| adaptive data broadcast in hybrid networks | k stathatos , n roussopoulos , js baras | proceedings of the international conference on very large & hellip ; , |   1997 |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1689
rtable_id: 15842
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:--------------------------------------|:---------------------------------------------------------------|:--------|-------:|
| computing iceberg queries efficiently | m fang , n shivakumar , h garcia-molina , r motwani , j ullman | vldb    |   1998 |

Entity B:
| title                                 | authors                        | venue                                                            | year   |
|:--------------------------------------|:-------------------------------|:-----------------------------------------------------------------|:-------|
| computing iceberg queries efficiently | n shivakumar , h garcia-molina | tech . report , stanford database group tech . report , november |        |



entityA: entityA.csv
entityB: entityB.csv
label: 1
original: ltable_id: 1651
rtable_id: 5889
label: 1
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 query language and optimization techniques for unstructured data | p buneman , s davidson , g hillebrand , d suciu | sigmod conference |   1996 |

Entity B:
| title                                                | authors                       | venue                       | year   |
|:-----------------------------------------------------|:------------------------------|:----------------------------|:-------|
| query optimization algorithm for semistructured data | d florescu , a levy , d suciu | submitted for publication , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1454
rtable_id: 18533
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:----------------------------------------------------------|:----------|:--------------|-------:|
| olap , relational , and multidimensional database systems | g colliat | sigmod record |   1996 |

Entity B:
| title                                                                      | authors                              | venue                                                      | year   |
|:---------------------------------------------------------------------------|:-------------------------------------|:-----------------------------------------------------------|:-------|
| cmd : a multidimensional declustering method for parallel database systems | l jianzhong , j srivastava , d rotem | proceedings of international conference on vldb , august , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 659
rtable_id: 12273
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-------------------------------------------------|:----------------------------------|:--------|-------:|
| type classification of semi-structured documents | m tresch , n palmer , a luniewski | vldb    |   1995 |

Entity B:
| title                                         | authors                                              | venue   | year   |
|:----------------------------------------------|:-----------------------------------------------------|:--------|:-------|
| structured multimedia document classification | l denoyer , jn vittaut , s brunessaux , s brunessaux |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 15
rtable_id: 16001
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 efficient algorithm for mining association rules in large databases | a savasere , e omiecinski , s navathe | vldb    |   1995 |

Entity B:
| title                                                                 | authors             | venue                                 |   year |
|:----------------------------------------------------------------------|:--------------------|:--------------------------------------|-------:|
| mining association rules with improved semantics in medical databases | m delgado , ma vila | artificial intelligence in medicine , |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1953
rtable_id: 43739
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:-----------------------|:----------|:--------|-------:|
| tutorial : data access |           |         |   2000 |

Entity B:
| title                          | authors   | venue                                                               | year   |
|:-------------------------------|:----------|:--------------------------------------------------------------------|:-------|
| tutorial : semistructured data | p buneman | proceedings of acm symposium on principles of database & hellip ; , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 872
rtable_id: 44664
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:---------------------------------------------------------|:-----------------------------------------|:--------|-------:|
| warehousing workflow data : challenges and opportunities | a bonifati , f casati , u dayal , m shan | vldb    |   2001 |

Entity B:
| title                                                   | authors       | venue           |   year |
|:--------------------------------------------------------|:--------------|:----------------|-------:|
| mining system audit data : opportunities and challenges | w lee , w fan | sigmod record , |   2001 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1272
rtable_id: 35962
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|:--------|-------:|
| semantic access : semantic interface for querying databases | n rishe , j yuan , r athauda , s chen , x lu , x ma , a vaschillo , a shaposhnikov , d vasilevsky | vldb    |   2000 |

Entity B:
| title                                        | authors       | venue              | year   |
|:---------------------------------------------|:--------------|:-------------------|:-------|
| an end user friendly interface for databases | i kamenyâ ? ¦ | proceedings vldb , |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1330
rtable_id: 4825
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:----------------------------------------------------|:-----------------------------|:------------------|-------:|
| wavelet-based histograms for selectivity estimation | y matias , j vitter , m wang | sigmod conference |   1998 |

Entity B:
| title                                                                          | authors                 | venue   | year   |
|:-------------------------------------------------------------------------------|:------------------------|:--------|:-------|
| compressed histograms with arbitrary bucket layouts for selectivity estimation | d fuchs , z he , bs lee |         |        |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 1172
rtable_id: 22744
label: 0
Task: Entity-Matching (source dataset: DBLP-Scholar)

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 |
|:----------------|:-----------|:--------------|-------:|
| editor 's notes | m franklin | sigmod record |   2000 |

Entity B:
| title           | authors     | venue                       |   year |
|:----------------|:------------|:----------------------------|-------:|
| editor 's notes | r goldstein | the american statistician , |   1996 |



entityA: entityA.csv
entityB: entityB.csv
label: 0
original: ltable_id: 329
rtable_id: 2235
label: 0