Triple

T20080834
Position Surface form Disambiguated ID Type / Status
Subject Octavio Paz E499994 entity
Predicate spouse P13 FINISHED
Object Marie-José Tramini NE NERFINISHED

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: Marie-José Tramini | Statement: [Octavio Paz, spouse, Marie-José Tramini]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marie-José Tramini
Context triple: [Octavio Paz, spouse, Marie-José Tramini]
  • A. Marie-José Tramini chosen
    Marie-José Tramini was a French-born artist and the second wife of Mexican Nobel laureate poet Octavio Paz, known for her work in collage and visual arts.
  • B. Marie-José Pérec
    Marie-José Pérec is a French sprinter and three-time Olympic champion, best known for dominating the 200m and 400m events in the 1990s.
  • C. Marie-José Nat
    Marie-José Nat was a French film and television actress known for her nuanced performances in mid-20th-century European cinema.
  • D. Marie-José Clivaz
    Marie-José Clivaz is an educator and school leader best known for co-founding the international private school Collège du Léman in Switzerland.
  • E. Marie-France Pisier
    Marie-France Pisier was a French actress and screenwriter renowned for her work in auteur cinema from the 1960s onward, notably in films by François Truffaut and André Téchiné.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da627770948190997f486f9a2e370f completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66557c19c8190b511857490bbd423 completed April 20, 2026, 5:41 p.m.
Created at: April 11, 2026, 3:41 p.m.