Triple

T5703451
Position Surface form Disambiguated ID Type / Status
Subject Count of Toulouse E125724 entity
Predicate region P40 FINISHED
Object Languedoc E498952 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: Languedoc | Statement: [Count of Toulouse, region, Languedoc]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Languedoc
Context triple: [Count of Toulouse, region, Languedoc]
  • A. Languedoc chosen
    Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
  • B. Gascogne
    Gascogne is a historic cultural region in southwestern France known for its Gascon language, rich rural traditions, and distinctive cuisine including Armagnac and foie gras.
  • C. Aquitaine
    Aquitaine is a historical region in southwestern France known for its rich medieval heritage, strategic importance in European power struggles, and later prominence as a center of culture and wine production.
  • D. Rouergue
    Rouergue is a historic cultural region in southern France, centered around the present-day Aveyron department and known for its rural landscapes, medieval towns, and Occitan heritage.
  • E. Provence
    Provence is a historic region in southeastern France known for its picturesque lavender fields, Mediterranean coastline, and rich cultural and culinary traditions.
  • 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_69c0082c96988190b3a6a201edce472a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02456efb48190bf3aaabcc77cda92 completed March 22, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07de28424819090ff1f4a4b6cc9c0 completed March 22, 2026, 11:40 p.m.
Created at: March 22, 2026, 3:45 p.m.