Triple

T12917017
Position Surface form Disambiguated ID Type / Status
Subject Viktor Navorski E309010 entity
Predicate helpsCharacter P7748 FINISHED
Object Enrique Cruz E1094911 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: Enrique Cruz | Statement: [Viktor Navorski, helpsCharacter, Enrique Cruz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Enrique Cruz
Context triple: [Viktor Navorski, helpsCharacter, Enrique Cruz]
  • A. Enrique Cruz chosen
    Enrique Cruz is a kindhearted airport food-service worker in the film "The Terminal" who befriends stranded traveler Viktor Navorski.
  • B. Armando Bermúdez
    Armando Bermúdez was a prominent Dominican figure after whom one of the country’s major national parks, located in the Cordillera Central, is named.
  • C. Miguel Briseño
    Miguel Briseño is a musician best known as a member of the American indie folk band Lord Huron.
  • D. Victor Hernández Cruz
    Victor Hernández Cruz is a Puerto Rican poet known for his innovative, jazz-influenced verse and his role as a prominent voice in contemporary Latino literature.
  • E. Guillermo Magaña
    Guillermo Magaña is a person notable enough to be recognized as a bearer of the surname Magaña, though specific widely known public details about him are not clearly established.
  • 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_69d7bdf92b588190acdf2a2291ac4590 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d971a1e8088190af697629baecf59f completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd54f5323c8190aa239bad461b0857 completed May 8, 2026, 3:13 a.m.
Created at: April 9, 2026, 5:41 p.m.