Triple

T14074976
Position Surface form Disambiguated ID Type / Status
Subject Dangerous When Wet E338707 entity
Predicate starring P1507 FINISHED
Object Fernando Lamas E578107 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: Fernando Lamas | Statement: [Dangerous When Wet, starring, Fernando Lamas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fernando Lamas
Context triple: [Dangerous When Wet, starring, Fernando Lamas]
  • A. Fernando Lamas chosen
    Fernando Lamas was an Argentine-American actor and director known for his suave, romantic leading roles in Hollywood films of the 1950s.
  • B. Alfredo Landa
    Alfredo Landa was a prominent Spanish film and television actor, celebrated for his versatile performances and for popularizing the "landismo" comedic style in Spanish cinema.
  • C. Mel Ferrer
    Mel Ferrer was an American actor, director, and producer known for his work in classic Hollywood films and his marriage to Audrey Hepburn.
  • D. Antonio Camargo
    Antonio Camargo is a Mexican geophysicist known for co-discovering the Chicxulub impact crater linked to the mass extinction of the dinosaurs.
  • E. Martín Ferres
    Martín Ferres is an Argentine bandoneon player best known as a core member of the neo-tango collective Bajofondo.
  • 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_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5c5bc49881909012b66fa451f495 completed April 14, 2026, 3:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb66eeb248190b73e992ab6b9af82 completed May 7, 2026, 3:57 p.m.
Created at: April 9, 2026, 10:21 p.m.