Triple

T18978635
Position Surface form Disambiguated ID Type / Status
Subject canton of Bethoncourt E464361 entity
Predicate administrativeCentre P1474 FINISHED
Object Bethoncourt NE NERFINISHED

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: Bethoncourt | Statement: [canton of Bethoncourt, administrativeCentre, Bethoncourt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bethoncourt
Context triple: [canton of Bethoncourt, administrativeCentre, Bethoncourt]
  • A. Bethoncourt chosen
    Bethoncourt is a commune in eastern France’s Bourgogne-Franche-Comté region, situated near Montbéliard in the Doubs department.
  • B. Estcourt
    Estcourt is a town in the KwaZulu-Natal province of South Africa, historically significant as a colonial settlement and regional agricultural and transport hub.
  • C. Seraincourt
    Seraincourt is a small commune in the Val-d'Oise department in the Île-de-France region of northern France.
  • D. Chantecoq
    Chantecoq is a former village in northeastern France that was submerged during the creation of the Lac du Der-Chantecoq reservoir.
  • E. Aigremont
    Aigremont is a small French commune located in the Yvelines department in north-central France.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd008af48190a97ff1c6488edf1b completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d621e3e08190b2d1d969ecaa380b completed April 20, 2026, 7:30 a.m.
Created at: April 10, 2026, noon