Triple

T16846560
Position Surface form Disambiguated ID Type / Status
Subject Anna Trebunskaya E409553 entity
Predicate DWTSPartner P34738 FINISHED
Object Michael Irvin E45683 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 Irvin | Statement: [Anna Trebunskaya, DWTSPartner, Michael Irvin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Irvin
Context triple: [Anna Trebunskaya, DWTSPartner, Michael Irvin]
  • A. Michael Irvin chosen
    Michael Irvin is a Hall of Fame former NFL wide receiver best known as a key offensive star of the Dallas Cowboys dynasty of the 1990s.
  • B. Ty Law
    Ty Law is a former NFL cornerback best known for his Pro Bowl career with the New England Patriots and induction into the Pro Football Hall of Fame.
  • C. Keyshawn Johnson
    Keyshawn Johnson is a former NFL wide receiver and Super Bowl champion who became a prominent sports media personality and radio host.
  • D. Deion Branch
    Deion Branch is a former NFL wide receiver best known for his standout performances with the New England Patriots, including earning Super Bowl MVP honors.
  • E. Darrell Green
    Darrell Green is a Hall of Fame NFL cornerback renowned for his exceptional speed and longevity during a 20-year career with Washington’s football franchise.
  • 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_69d883952b048190887740a980b712ed completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b354eaf081908fe6f84a330d7866 completed April 18, 2026, 4:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb1b47648190909eaaf4e1e8e4c3 completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:24 a.m.