Triple

T12386014
Position Surface form Disambiguated ID Type / Status
Subject Matthew Holworthy E295865 entity
Predicate hasGivenName P17 FINISHED
Object Matthew
Matthew is a common masculine given name of Hebrew origin, widely used in English-speaking countries.
E556162 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Matthew | Statement: [Matthew Holworthy, hasGivenName, Matthew]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matthew
Context triple: [Matthew Holworthy, hasGivenName, Matthew]
  • A. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • B. John
    John is the given name of John Randolph Hearst, a member of the prominent Hearst family associated with American media and publishing.
  • C. John
    John Stallworth is a former American football wide receiver best known for his Hall of Fame career with the Pittsburgh Steelers during their 1970s dynasty.
  • D. John
    John "Jack" Pfiester was an early 20th-century American Major League Baseball pitcher, best known for his standout seasons with the Chicago Cubs during their dominant era.
  • E. John
    John G. Kemeny was a Hungarian-American mathematician and computer scientist best known as the co-inventor of the BASIC programming language and former president of Dartmouth College.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Matthew
Triple: [Matthew Holworthy, hasGivenName, Matthew]
Generated description
Matthew is a common masculine given name of Hebrew origin, widely used in English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Matthew
Target entity description: Matthew is a common masculine given name of Hebrew origin, widely used in English-speaking countries.
  • A. Matthew chosen
    Matthew is a masculine given name of Hebrew origin, commonly used in English-speaking countries and meaning "gift of God."
  • B. Matthew
    Matthew is the given name of Sir Matt Busby, the legendary Scottish football manager best known for his long and successful tenure at Manchester United.
  • C. Matthew
    Matthew is the full given name of American television journalist and former "Today" show co-host Matt Lauer.
  • D. Matthew
    Matthew is the given name of the pioneering British Egyptologist and archaeologist Flinders Petrie, renowned for developing systematic excavation and seriation methods.
  • E. Matthew
    Matthew is the full given name of American former professional stock car racing driver Matt Kenseth, a NASCAR Cup Series champion.
  • F. None of above.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d6ad9e653c8190b1473c860ee53dae completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d93fbd489c819098233a111442762e completed April 10, 2026, 6:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f62ac939bc819081629b9eef20c4e7 completed May 2, 2026, 4:48 p.m.
NEDg Description generation batch_69f62c7b28588190839c35c19856d16f completed May 2, 2026, 4:55 p.m.
NED2 Entity disambiguation (via description) batch_69f62e403a308190a2bba3fefc420932 completed May 2, 2026, 5:02 p.m.
Created at: April 8, 2026, 9:54 p.m.