Triple

T10189512
Position Surface form Disambiguated ID Type / Status
Subject Chambéry Airport E237995 entity
Predicate serves P98 FINISHED
Object Chambéry E46643 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: Chambéry | Statement: [Chambéry Airport, serves, Chambéry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chambéry
Context triple: [Chambéry Airport, serves, Chambéry]
  • A. Chambéry chosen
    Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
  • B. Grenoble
    Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
  • C. Aix-les-Bains
    Aix-les-Bains is a French spa and resort town in the Savoie department, renowned for its thermal baths and lakeside setting on the edge of the Alps.
  • D. Briançon
    Briançon is a fortified alpine town in southeastern France, known as one of the highest cities in Europe and a key historical stronghold near the Italian border.
  • E. Champéry
    Champéry is a Swiss alpine village and ski resort in the canton of Valais, known for its access to the Portes du Soleil ski area and mountain tourism.
  • 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_69ca84de1b208190bf17bb305b002605 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded7d6fdc81908052866495b6574f completed April 2, 2026, 4:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69d98811fd3881909369e0f00f2a8267 completed April 10, 2026, 11:30 p.m.
Created at: March 30, 2026, 9:12 p.m.