Triple

T16161799
Position Surface form Disambiguated ID Type / Status
Subject Mr. Box Office E392196 entity
Predicate stars P1956 FINISHED
Object Bill Bellamy E620811 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: Bill Bellamy | Statement: [Mr. Box Office, stars, Bill Bellamy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bill Bellamy
Context triple: [Mr. Box Office, stars, Bill Bellamy]
  • A. Bill Bellamy chosen
    Bill Bellamy is an American stand-up comedian and actor known for his work on MTV in the 1990s and roles in films like "How to Be a Player" and "Love Jones."
  • B. John Hough
    John Hough is a British film and television director best known for his work in horror and genre cinema during the 1970s and 1980s.
  • C. Bill Milner
    Bill Milner is a British actor known for roles in films such as "Son of Rambow," "X-Men: First Class," and various television dramas.
  • D. Tony Gillingham
    Tony Gillingham is a wealthy and charming aristocrat in Downton Abbey who becomes one of Lady Mary Crawley’s principal suitors.
  • E. Frank Bannister
    Frank Bannister is a psychic investigator and con artist who can see and communicate with ghosts in the horror-comedy film "The Frighteners."
  • 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_69d87f1d32208190942e4e499a80c18c completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e21e5ffba88190b9dc7bb9afb6fdf2 completed April 17, 2026, 11:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69fff7b33f3481909fe856b8be7d9bcd completed May 10, 2026, 3:12 a.m.
Created at: April 10, 2026, 5:02 a.m.