Triple

T17340350
Position Surface form Disambiguated ID Type / Status
Subject Bed and Board E421049 entity
Predicate producer P490 FINISHED
Object Marcel Berbert 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: Marcel Berbert | Statement: [Bed and Board, producer, Marcel Berbert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marcel Berbert
Context triple: [Bed and Board, producer, Marcel Berbert]
  • A. Marcel Berbert chosen
    Marcel Berbert was a French film producer known for his collaborations with prominent directors such as François Truffaut during the mid-20th century.
  • B. José Oubrerie
    José Oubrerie is a French architect and former collaborator of Le Corbusier, known for completing and interpreting several of Le Corbusier’s unfinished projects.
  • C. Bruno Barbey
    Bruno Barbey was a renowned French-Moroccan photojournalist celebrated for his vivid color photography and extensive work documenting political unrest and cultural life around the world.
  • D. Boris Dilliès
    Boris Dilliès is a Belgian politician known for serving as the mayor of the Brussels municipality of Uccle.
  • E. Marcel Weber
    Marcel Weber is a personal name shared by multiple individuals, most commonly of German-speaking or Swiss origin, and may refer to various figures in fields such as sports, academia, or the arts.
  • 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_69d889d3adc881909319f1edb8d2a956 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a14ec90819098db2ac0d58a53e1 completed April 19, 2026, 2:12 a.m.
Created at: April 10, 2026, 5:44 a.m.