Triple

T16234518
Position Surface form Disambiguated ID Type / Status
Subject MVP Track Club E394069 entity
Predicate hasAthlete P17934 FINISHED
Object Michael Frater E1206918 NE FINISHED

How this triple was built (2 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: Michael Frater | Statement: [MVP Track Club, hasAthlete, Michael Frater]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Frater
Context triple: [MVP Track Club, hasAthlete, Michael Frater]
  • A. Michael Frater chosen
    Michael Frater is a Jamaican sprinter specializing in the 100 metres who has been a key member of Jamaica’s world-record-setting and Olympic medal-winning 4 × 100 metres relay teams.
  • B. Michael Jeffery
    Michael Jeffery was a British music manager best known for managing Jimi Hendrix and co-founding Electric Lady Studios in New York City.
  • C. Kevin Stoney
    Kevin Stoney was a British character actor best known for his villainous roles in classic science fiction television, particularly in series like Doctor Who.
  • D. Phil Jordan
    Phil Jordan is a musician best known as a former member of the American rock band No Doubt.
  • E. Darryl Hickman
    Darryl Hickman is an American former child actor and film and television performer known for roles in classic Hollywood films and later work as a television executive and acting coach.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d87f204df88190a8f88923decf9835 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24559af48819092e4b466778b07e2 completed April 17, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00354a20d081908288fb8c0e8b83b6 completed May 10, 2026, 7:35 a.m.
Created at: April 10, 2026, 5:04 a.m.