Triple

T16876166
Position Surface form Disambiguated ID Type / Status
Subject Merci pour le chocolat E421303 entity
Predicate castMember P1668 FINISHED
Object Michel Robin 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: Michel Robin | Statement: [Merci pour le chocolat, castMember, Michel Robin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michel Robin
Context triple: [Merci pour le chocolat, castMember, Michel Robin]
  • A. Michel Robin chosen
    Michel Robin was a French character actor known for his extensive work in film, television, and theater, often appearing in historical and dramatic roles.
  • B. Jean-Claude Colin
    Jean-Claude Colin was a 19th-century French Catholic priest and founder of the Society of Mary (Marists), known for his role in the post-Revolution revival of religious life in France.
  • C. Michel Andrault
    Michel Andrault was a prominent French architect known for his influential large-scale housing and urban development projects in the late 20th century.
  • D. Michel Py
    Michel Py is a French local politician best known for serving as the long-time mayor of the Mediterranean commune of Leucate.
  • E. Pierre Morel
    Pierre Morel is a French film director and cinematographer best known for high-octane action movies such as "Taken" and "District 13."
  • 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_69d889d470fc8190b4aec199636c0c56 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3b7f646308190b5e277b5f51cd315 completed April 18, 2026, 4:57 p.m.
Created at: April 10, 2026, 5:29 a.m.