Triple

T16761023
Position Surface form Disambiguated ID Type / Status
Subject The Young Girls of Rochefort E407342 entity
Predicate featuresCharacter P626 FINISHED
Object Solange Garnier E928293 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: Solange Garnier | Statement: [The Young Girls of Rochefort, featuresCharacter, Solange Garnier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Solange Garnier
Context triple: [The Young Girls of Rochefort, featuresCharacter, Solange Garnier]
  • A. Victoria Legrand
    Victoria Legrand is a French-American musician best known as the vocalist and keyboardist of the dream pop duo Beach House.
  • B. Giselle Itié
    Giselle Itié is a Brazilian-Mexican actress known for her work in Brazilian television and film as well as roles in international action movies.
  • C. Anne Lauvergeon
    Anne Lauvergeon is a French business executive best known for leading the nuclear energy company Areva and for her prominent role in France’s industrial and energy sectors.
  • D. Yrétha Silété
    Yrétha Silété is a French figure skater known for competing at major international events, including the European Figure Skating Championships.
  • E. Nicole Maurey chosen
    Nicole Maurey was a French film and television actress known for her work in European cinema from the 1940s through the 1970s.
  • 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_69d8839174188190909f190097207065 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3abec638c81909d71ff452a4123c9 completed April 18, 2026, 4:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00a52d077081908080c61da67e0032 completed May 10, 2026, 3:33 p.m.
Created at: April 10, 2026, 5:21 a.m.