Triple

T12347872
Position Surface form Disambiguated ID Type / Status
Subject Meldrick Taylor E294404 entity
Predicate familyName P18 FINISHED
Object Taylor
Taylor is the surname of Meldrick Taylor, an American former professional boxer and Olympic gold medalist known for his speed and technical skill.
E63210 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: Taylor | Statement: [Meldrick Taylor, familyName, Taylor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taylor
Context triple: [Meldrick Taylor, familyName, Taylor]
  • A. John
    John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
  • B. John
    John I, Count of Holland, was a medieval nobleman who ruled the County of Holland at the turn of the 14th century.
  • C. John
    John McDowell is a prominent South African-born philosopher known for his influential work in epistemology, philosophy of mind, and ethics.
  • D. John
    John Cicero was a late 15th-century Elector of Brandenburg from the House of Hohenzollern who helped consolidate the territory’s political and administrative structures within the Holy Roman Empire.
  • E. John
    John Brabourne was a British film and television producer and peer, known for producing works such as the 1979 adaptation of "Murder on the Orient Express."
  • 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: Taylor
Triple: [Meldrick Taylor, familyName, Taylor]
Generated description
Taylor is the surname of Meldrick Taylor, an American former professional boxer and Olympic gold medalist known for his speed and technical skill.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Taylor
Target entity description: Taylor is the surname of Meldrick Taylor, an American former professional boxer and Olympic gold medalist known for his speed and technical skill.
  • A. Taylor chosen
    Taylor is a common English surname borne by numerous notable individuals across fields such as politics, arts, sports, and academia.
  • B. Taylor
    Taylor is a suburban city in Wayne County, Michigan, known for its residential communities and proximity to Detroit.
  • C. Tyler
    Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "house builder."
  • D. Tyler
    Tyler is the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
  • E. Tyler
    Tyler is a character in the 2015 horror-thriller film "The Visit," serving as one of the two grandchildren whose unsettling stay with their grandparents drives the movie’s plot.
  • 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_69d6ab6ccbec8190b09e2d357aa80064 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f7ba17481908b03af7316b28d9b completed April 10, 2026, 6:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f62aae8d7c8190a722c28a5a153d1d completed May 2, 2026, 4:47 p.m.
NEDg Description generation batch_69f62c569a6c8190aecf8a4c627d8893 completed May 2, 2026, 4:54 p.m.
NED2 Entity disambiguation (via description) batch_69f62d13237881908b7c2dca173e20cf completed May 2, 2026, 4:57 p.m.
Created at: April 8, 2026, 9:53 p.m.