Triple

T2505113
Position Surface form Disambiguated ID Type / Status
Subject Arpitan E52559 entity
Predicate spokenIn P2266 FINISHED
Object Bresse E160069 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: Bresse | Statement: [Arpitan, spokenIn, Bresse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bresse
Context triple: [Arpitan, spokenIn, Bresse]
  • A. Bresse chosen
    Bresse is a historical region in eastern France known for its rich agricultural land, distinctive culinary traditions, and cultural ties to the Franco-Provençal linguistic area.
  • B. Brioude
    Brioude is a historic town in south-central France known for its Romanesque Basilica of Saint-Julien and its location in the Haute-Loire department of the Auvergne region.
  • C. Tournus
    Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
  • D. Beaune
    Beaune is a historic town in eastern France renowned as the wine capital of Burgundy and a center of Burgundy’s prestigious vineyards and wine trade.
  • E. Mâconnais
    Mâconnais is a wine-producing subregion in southern Burgundy, France, best known for its Chardonnay-based white wines.
  • 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_69ab4957b3a88190adf968ae0c1b931c completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd1cec9f48190848b6129aa394ce4 completed March 7, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69b276b188248190b015eec2b83bc20b completed March 12, 2026, 8:17 a.m.
Created at: March 6, 2026, 9:46 p.m.