Triple

T17375305
Position Surface form Disambiguated ID Type / Status
Subject Montpelier E422420 entity
Predicate namedFor P63 FINISHED
Object Montpelier, France 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: Montpelier, France | Statement: [Montpelier, namedFor, Montpelier, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montpelier, France
Context triple: [Montpelier, namedFor, Montpelier, France]
  • A. Montpellier, France chosen
    Montpellier, France is a historic and vibrant city in southern France near the Mediterranean coast, known for its medieval architecture, large student population, and role as a regional cultural and economic center.
  • B. Villeblevin, France
    Villeblevin, France is a small commune in north-central France best known as the place where Nobel Prize–winning writer Albert Camus died in a car accident.
  • C. Pontivy, France
    Pontivy is a historic town in the Morbihan department of Brittany in northwestern France, known for its medieval and Napoleonic heritage along the Blavet River.
  • D. Leon, France
    Leon, France is a French locality whose name was used as the namesake for the town of Leon in Iowa, United States.
  • E. Chaumont, France
    Chaumont, France is a historic town in northeastern France known for its role as the World War I headquarters of the American Expeditionary Forces.
  • 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_69d889d6535c81908be333c01deaec4e completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a6c864481908507290282cc6d25 completed April 19, 2026, 2:14 a.m.
Created at: April 10, 2026, 5:44 a.m.