Triple

T6139742
Position Surface form Disambiguated ID Type / Status
Subject Michael Vartan E136929 entity
Predicate relative P37 FINISHED
Object Sylvie Vartan E374883 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: Sylvie Vartan | Statement: [Michael Vartan, relative, Sylvie Vartan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sylvie Vartan
Context triple: [Michael Vartan, relative, Sylvie Vartan]
  • A. Sylvie Vartan chosen
    Sylvie Vartan is a Bulgarian-born French pop singer and actress who became one of France’s most popular yé-yé idols in the 1960s.
  • B. Claudine Longet
    Claudine Longet is a French-born singer and actress known for her soft, breathy vocal style and appearances in 1960s American television and film.
  • C. Julie Gayet
    Julie Gayet is a French actress and film producer known for her work in cinema and for her high-profile relationship with former French president François Hollande.
  • D. Françoise Rosay
    Françoise Rosay was a prominent French stage and film actress known for her powerful character roles in European cinema from the 1920s through the 1950s.
  • E. Nelly Roussel
    Nelly Roussel was a pioneering French feminist, neo-Malthusian activist, and orator known for her advocacy of birth control, women’s rights, and social reform in the early 20th century.
  • 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_69c008a179388190a3b5a081bbf46d55 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05cb030fc8190b78e4967eea65611 completed March 22, 2026, 9:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c135ecd62c8190911b98133bf71dfc completed March 23, 2026, 12:45 p.m.
Created at: March 22, 2026, 4:15 p.m.