Triple

T7615616
Position Surface form Disambiguated ID Type / Status
Subject Mel Ferrer E172353 entity
Predicate name P16 FINISHED
Object Mel Ferrer E172353 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: Mel Ferrer | Statement: [Mel Ferrer, name, Mel Ferrer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mel Ferrer
Context triple: [Mel Ferrer, name, Mel Ferrer]
  • A. Mel Ferrer chosen
    Mel Ferrer was an American actor, director, and producer known for his work in classic Hollywood films and his marriage to Audrey Hepburn.
  • B. José Ferrer
    José Ferrer was a Puerto Rican-born actor and director renowned for his Oscar-winning performance in "Cyrano de Bergerac" and his distinguished career on stage and screen.
  • C. Fernando Lamas
    Fernando Lamas was an Argentine-American actor and director known for his suave, romantic leading roles in Hollywood films of the 1950s.
  • D. Victor Jory
    Victor Jory was a Canadian-born American character actor known for his distinctive deep voice and frequent portrayals of villains in film, television, and theater during the mid-20th century.
  • E. Henry Silva
    Henry Silva was an American character actor known for his intense, often villainous roles in films such as "The Manchurian Candidate" and numerous crime and action movies from the 1950s through the 1990s.
  • 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_69c6994f50808190ba228764bb422417 completed March 27, 2026, 2:50 p.m.
NER Named-entity recognition batch_69c6fa4569c88190b2968403a24e7882 completed March 27, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8686d16808190bc431c43c0928f6e completed March 28, 2026, 11:46 p.m.
Created at: March 27, 2026, 3:55 p.m.