Triple

T2664498
Position Surface form Disambiguated ID Type / Status
Subject Modane E55601 entity
Predicate hasRailConnectionTo P848 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: [Modane, hasRailConnectionTo, Chambéry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chambéry
Context triple: [Modane, hasRailConnectionTo, 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. Échirolles
    Échirolles is a suburban commune in southeastern France, forming part of the Grenoble metropolitan area in the Isère department.
  • 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_69ab49e54de48190be708cd1cf8be073 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd96dba44819085c3e651afba7806 completed March 7, 2026, 7:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69b34ba6d2cc819091d748b8af7ccbe6 completed March 12, 2026, 11:26 p.m.
Created at: March 6, 2026, 9:54 p.m.