Triple

T10688688
Position Surface form Disambiguated ID Type / Status
Subject Audrey Kathleen Ruston E251948 entity
Predicate spouse P13 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: [Audrey Kathleen Ruston, spouse, Mel Ferrer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mel Ferrer
Context triple: [Audrey Kathleen Ruston, spouse, 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_69d6aa5bd7c08190a816e733b4045c23 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fd1aef888190ba92474af3a49e36 completed April 9, 2026, 1:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d9889d1f988190938be54771161b00 completed April 10, 2026, 11:32 p.m.
Created at: April 8, 2026, 9:11 p.m.