Triple

T13240336
Position Surface form Disambiguated ID Type / Status
Subject Good Behavior E315262 entity
Predicate hasMainCharacter P1183 FINISHED
Object Javier Pereira E1029428 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: Javier Pereira | Statement: [Good Behavior, hasMainCharacter, Javier Pereira]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Javier Pereira
Context triple: [Good Behavior, hasMainCharacter, Javier Pereira]
  • A. Javier Pereira chosen
    Javier Pereira is a fictional character from the television series "Good Behavior."
  • B. Óscar Pareja
    Óscar Pareja is a Colombian soccer manager and former midfielder known for his successful coaching stints in Major League Soccer, including with FC Dallas and Orlando City SC.
  • C. José Antonio Camacho
    José Antonio Camacho is a former Spanish footballer and manager best known for his long tenure as a defender at Real Madrid and for coaching the Spanish national team.
  • D. Luis Fernandez
    Luis Fernandez is a former French footballer and manager best known as part of France’s celebrated 1980s midfield and for coaching clubs such as Paris Saint-Germain.
  • E. Reinaldo Rueda
    Reinaldo Rueda is a Colombian football manager known for coaching several Latin American national teams and leading them to major international tournaments.
  • 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_69d806b1072881909e46bd212259c5f0 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d5850ac8190849a51da39efe5be completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f70a38d09881909e8e3c32e9b1746e completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:23 p.m.