Triple

T3272048
Position Surface form Disambiguated ID Type / Status
Subject France 3 E68669 entity
Predicate hasPart P35 FINISHED
Object France 3 Régions 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 Régions | Statement: [France 3, hasPart, France 3 Régions]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: France 3 Régions
Context triple: [France 3, hasPart, France 3 Régions]
  • 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 4
    France 4 is a French public television channel, part of the France Télévisions group, known for broadcasting youth-oriented and family entertainment programming.
  • C. Hauts-de-France
    Hauts-de-France is a region in northern France known for its industrial heritage, coastal areas along the English Channel, and proximity to Belgium and the United Kingdom.
  • D. French regions
    French regions are the primary administrative and territorial divisions of France, each grouping several departments under a regional council for governance and planning.
  • E. Auvergne-Rhône-Alpes region
    The Auvergne-Rhône-Alpes region is a large administrative region in east-central France known for its major cities like Lyon and Grenoble, diverse landscapes from the Alps to volcanic highlands, and strong industrial and agricultural economy.
  • 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_69ad859b54f881909bf530d549caf2fd completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adaff6308881908886a44804a0bb09 completed March 8, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b28f0793e08190af55ee16e5091451 completed March 12, 2026, 10:01 a.m.
Created at: March 8, 2026, 3:10 p.m.