Triple

T9545970
Position Surface form Disambiguated ID Type / Status
Subject Patrick de Carolis E230285 entity
Predicate employer P7 FINISHED
Object France 3 E68669 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: France 3 | Statement: [Patrick de Carolis, employer, France 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: France 3
Context triple: [Patrick de Carolis, employer, France 3]
  • A. France 3 chosen
    France 3 is a French public television channel known for its regional programming and news coverage as part of the France Télévisions group.
  • B. France 2
    France 2 is a major French public television channel that forms part of the France Télévisions group and broadcasts a wide range of news, entertainment, and cultural programming nationwide.
  • C. France 3 Cinéma
    France 3 Cinéma is a French film production company associated with the public television network France 3, known for co-producing a wide range of auteur and independent films.
  • D. France 4
    France 4 is a French public television channel, part of the France Télévisions group, known for broadcasting youth-oriented and family entertainment programming.
  • E. France 5
    France 5 is a French public television channel known for its focus on educational, cultural, and documentary programming.
  • 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_69ca847c70b8819088a0a0bad64a50d6 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9902fca081909125660ae6336d3f completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14c747e608190b2fa470324fff454 completed April 4, 2026, 5:37 p.m.
Created at: March 30, 2026, 8:02 p.m.