Triple

T13236421
Position Surface form Disambiguated ID Type / Status
Subject Golden Globe Award for World Film Favorite – Male E315156 entity
Predicate notableWinner P2766 FINISHED
Object Roger Moore E105584 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: Roger Moore | Statement: [Golden Globe Award for World Film Favorite – Male, notableWinner, Roger Moore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Roger Moore
Context triple: [Golden Globe Award for World Film Favorite – Male, notableWinner, Roger Moore]
  • A. Roger Moore chosen
    Roger Moore was an English actor best known for playing James Bond in seven films from 1973 to 1985.
  • B. George Lazenby
    George Lazenby is an Australian actor best known for playing James Bond in the 1969 film "On Her Majesty's Secret Service."
  • C. Timothy Dalton
    Timothy Dalton is a British actor best known for portraying James Bond in the films "The Living Daylights" and "Licence to Kill."
  • D. Robert Vaughn
    Robert Vaughn was an American actor best known for his suave, sophisticated roles in film and television, particularly as Napoleon Solo in the 1960s series "The Man from U.N.C.L.E."
  • E. Sean Connery
    Sean Connery was a Scottish actor best known for originating the role of James Bond on film and for his distinguished career in both mainstream and critically acclaimed cinema.
  • 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_69d806b1072881909e46bd212259c5f0 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d56da008190af55da3a9e7ffd4d completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff3079c08190977663e5d4762a80 completed May 3, 2026, 7:54 a.m.
Created at: April 9, 2026, 9:22 p.m.