Triple

T14628856
Position Surface form Disambiguated ID Type / Status
Subject Matt Le Tissier E343425 entity
Predicate givenName P17 FINISHED
Object Matthew
Matthew is the given name of Matt Le Tissier, the renowned former Southampton and England footballer known for his exceptional skill and loyalty to a single club.
E1110995 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: [Matt Le Tissier, givenName, Matthew]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matthew
Context triple: [Matt Le Tissier, givenName, Matthew]
  • A. John
    John Vassall Jr. was a British civil servant who became notorious as a Soviet spy during the Cold War.
  • B. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • C. John
    John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
  • D. John
    John is the given name of John Bowen, a British novelist and playwright known for his crime and speculative fiction.
  • E. John
    John is the given name of John Boyle O'Reilly, a 19th-century Irish-born poet, journalist, and civil rights activist who became influential in the United States.
  • 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: [Matt Le Tissier, givenName, Matthew]
Generated description
Matthew is the given name of Matt Le Tissier, the renowned former Southampton and England footballer known for his exceptional skill and loyalty to a single club.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Matthew
Target entity description: Matthew is the given name of Matt Le Tissier, the renowned former Southampton and England footballer known for his exceptional skill and loyalty to a single club.
  • A. 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.
  • B. Matthew
    Matthew is the given name of the pioneering British Egyptologist and archaeologist Flinders Petrie, renowned for developing systematic excavation and seriation methods.
  • C. Matthew
    Matthew is a masculine given name of Hebrew origin, commonly used in English-speaking countries and meaning "gift of God."
  • D. Matthew
    Matthew is the given name of blues guitarist Matt "Guitar" Murphy, renowned for his work with the Blues Brothers and numerous Chicago blues legends.
  • 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. chosen

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_69d822dffc3c8190aa173b90761bffda completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb4a7c8fc81909d10c1f563d7d1e7 completed April 14, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fda92c25ac8190ba931c009e7ace19 completed May 8, 2026, 9:13 a.m.
NEDg Description generation batch_69fdb7e5aa6481908d4933e3932c5d03 completed May 8, 2026, 10:16 a.m.
NED2 Entity disambiguation (via description) batch_69fdb841f3a88190867c635950a1492c completed May 8, 2026, 10:17 a.m.
Created at: April 10, 2026, 1:26 a.m.