Triple
T415168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auvergne-Rhône-Alpes |
E9576
|
entity |
| Predicate | containsCity |
P294
|
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: [Auvergne-Rhône-Alpes, containsCity, Chambéry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chambéry Context triple: [Auvergne-Rhône-Alpes, containsCity, 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.
Thonon-les-Bains
Thonon-les-Bains is a French spa and resort town in the Haute-Savoie region, known for its lakeside setting on Lake Geneva and views of the Alps.
-
D.
Besançon
Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
-
E.
Lancy
Lancy is a suburban municipality in western Switzerland that forms part of the urban area of Geneva.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ee8d835881908403ea23901e52b3 |
completed | Feb. 28, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a68916d9f081909a14612144491fb7 |
completed | March 3, 2026, 7:09 a.m. |
Created at: Feb. 28, 2026, 1:09 p.m.