Triple

T14806617
Position Surface form Disambiguated ID Type / Status
Subject Captain Vidal E348053 entity
Predicate kills P19780 FINISHED
Object Doctor Ferreiro E343683 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: Doctor Ferreiro | Statement: [Captain Vidal, kills, Doctor Ferreiro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Doctor Ferreiro
Context triple: [Captain Vidal, kills, Doctor Ferreiro]
  • A. Doctor Ferreiro chosen
    Doctor Ferreiro is a compassionate and principled physician in the film "Pan's Labyrinth," who secretly aids the anti-Franco resistance.
  • B. Dr. Casares
    Dr. Casares is a compassionate, aging doctor and caretaker at a remote orphanage in Guillermo del Toro’s gothic horror film "The Devil’s Backbone."
  • C. Dr. Vertiz
    Dr. Vertiz is a Mexico City Metrobús station serving the Colonia Narvarte area.
  • D. Dr. Vigil
    Dr. Vigil is a compassionate and clear-sighted doctor who serves as a moral and rational counterpoint to the Consul’s self-destruction in Malcolm Lowry’s novel "Under the Volcano."
  • E. Doctor De Soto
    Doctor De Soto is a popular children's picture book by William Steig about a kind, clever mouse dentist who outwits a dangerous fox patient.
  • 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_69d822ea8b7c819097dfadf3d45545e6 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decf33b6a08190ab6a4cfeda2cc09c completed April 14, 2026, 11:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24c6b3008190a0fac1dace40361a completed May 8, 2026, 6 p.m.
Created at: April 10, 2026, 1:40 a.m.