Triple

T6686140
Position Surface form Disambiguated ID Type / Status
Subject Deux-Sèvres E152102 entity
Predicate borders P224 FINISHED
Object Charente E298483 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: Charente | Statement: [Deux-Sèvres, borders, Charente]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charente
Context triple: [Deux-Sèvres, borders, Charente]
  • A. Charente chosen
    Charente is a department in southwestern France known for its historic towns, cognac production, and scenic river landscapes.
  • B. Gironde
    Gironde is a department in southwestern France that encompasses much of the Bordeaux wine region, including renowned appellations such as Graves.
  • C. Loiret
    Loiret is a department in north-central France, named after the Loiret River and known for its historic towns and proximity to the Loire Valley.
  • D. Cère
    Cère is a river in south-central France that flows through the Cantal department as a tributary of the Dordogne.
  • E. Dordogne River
    The Dordogne River is a major river in south-central and southwestern France, renowned for its scenic valleys, historic towns, and role in the region’s cultural and natural heritage.
  • 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_69c687f9977c819097e7f5ada4fe522e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b14cd6748190aad4badd5f253478 completed March 27, 2026, 4:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70af0c0e4819094c89193a222a6c9 completed March 27, 2026, 10:55 p.m.
Created at: March 27, 2026, 2:04 p.m.