Triple

T348037
Position Surface form Disambiguated ID Type / Status
Subject Bordeaux E6982 entity
Predicate hasRiver P165 FINISHED
Object Dordogne E42430 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: Dordogne | Statement: [Bordeaux, hasRiver, Dordogne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dordogne
Context triple: [Bordeaux, hasRiver, Dordogne]
  • A. Touraine
    Touraine is a historic region in central France, famed for its Loire Valley châteaux, wine production, and role as a former royal heartland.
  • B. Val-d'Oise
    Val-d'Oise is a department in northern France that forms part of the Paris metropolitan region and includes both suburban areas and rural landscapes.
  • C. Haute-Loire
    Haute-Loire is a rural department in south-central France, known for its volcanic landscapes, the upper Loire River valley, and historic towns such as Le Puy-en-Velay.
  • D. Nouvelle-Aquitaine chosen
    Nouvelle-Aquitaine is the largest administrative region of France, located in the southwest and known for its Atlantic coastline, wine regions like Bordeaux, and diverse cultural and natural landscapes.
  • E. Creuse
    Creuse is a rural department in central France known for its sparsely populated landscapes, traditional agriculture, and part of the historic Limousin region.
  • 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eb1c1c908190b3a01de893207ed1 completed Feb. 28, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69a413ee31fc81908cbda4c0737d1b33 completed March 1, 2026, 10:24 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.