Triple

T20157606
Position Surface form Disambiguated ID Type / Status
Subject Moonraker E491614 entity
Predicate featuresCharacter P626 FINISHED
Object Bill Tanner NE NERFINISHED

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: Bill Tanner | Statement: [Moonraker, featuresCharacter, Bill Tanner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bill Tanner
Context triple: [Moonraker, featuresCharacter, Bill Tanner]
  • A. Bill Tanner chosen
    Bill Tanner is a recurring fictional British Secret Service official in the James Bond series, typically portrayed as M’s chief of staff and a close ally of 007.
  • B. Dick Tufeld
    Dick Tufeld was an American voice actor best known as the iconic voice of the Robot in the classic television series "Lost in Space."
  • C. Tosh Berman
    Tosh Berman is an American writer, poet, and publisher known for his memoirs and works on art, music, and culture, as well as his involvement in the Los Angeles avant-garde scene.
  • D. Bill Townsend
    Bill Townsend is a fictional character from the Netflix series "Sweet Magnolias," known as Maddie Townsend’s ex-husband and a central figure in the show’s family drama.
  • E. Bill Tierney
    Bill Tierney is a Hall of Fame American lacrosse coach renowned for leading multiple NCAA championship teams, most notably at Princeton and later at the University of Denver.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6265f8f0819080b29c752a574088 completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e667e18a0c8190a2cc2b305da28047 completed April 20, 2026, 5:52 p.m.
Created at: April 11, 2026, 11:34 p.m.