Triple

T9810902
Position Surface form Disambiguated ID Type / Status
Subject Chartreuse Mountains E238266 entity
Predicate nearCity P350 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: [Chartreuse Mountains, nearCity, Chambéry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chambéry
Context triple: [Chartreuse Mountains, nearCity, 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_69ca84defac48190abc1148804f184c1 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb2214a7c8190b516acf64e2b85db completed April 2, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eacbc2348190bac1cc7f41a389b9 completed April 5, 2026, 4:53 a.m.
Created at: March 30, 2026, 8:30 p.m.