Triple

T22962082
Position Surface form Disambiguated ID Type / Status
Subject In Search of Fellini E570927 entity
Predicate castMember P1668 FINISHED
Object Barbara Bouchet 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: Barbara Bouchet | Statement: [In Search of Fellini, castMember, Barbara Bouchet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barbara Bouchet
Context triple: [In Search of Fellini, castMember, Barbara Bouchet]
  • A. Barbara Bouchet chosen
    Barbara Bouchet is a German-American actress and former model best known for her roles in 1960s–70s European cinema, including numerous Italian giallo and crime films.
  • B. Françoise Brion
    Françoise Brion is a French actress known for her work in European cinema from the 1960s onward, including collaborations with prominent auteurs.
  • C. Micheline Presle
    Micheline Presle is a renowned French actress known for her prolific film and television career spanning from the 1940s onward.
  • D. Catherine Frot
    Catherine Frot is a French actress acclaimed for her work in film, television, and theater, known for both her comedic talent and dramatic roles.
  • E. Michèle Morgan
    Michèle Morgan was a renowned French film actress celebrated for her expressive blue eyes and leading roles in classic European cinema from the 1930s through the 1960s.
  • 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_69e245b212a88190b5259caf51606084 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f181f594fc8190816418486b798198 completed April 29, 2026, 3:58 a.m.
Created at: April 17, 2026, 3:47 p.m.