Triple
T10215426
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Massif des Bauges |
E242427
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Aix-les-Bains |
E321327
|
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: Aix-les-Bains | Statement: [Massif des Bauges, nearCity, Aix-les-Bains]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aix-les-Bains Context triple: [Massif des Bauges, nearCity, Aix-les-Bains]
-
A.
Aix-les-Bains
chosen
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.
-
B.
Grenoble
Grenoble is a major city in southeastern France, known for its Alpine setting, universities, and research centers.
-
C.
Clermont-Ferrand
Clermont-Ferrand is a central French city known for its historic cathedral built of black volcanic stone and as the longtime headquarters of the tire company Michelin.
-
D.
Meyzieu
Meyzieu is a suburban commune in eastern France, located near Lyon and known for its residential character and proximity to major transport links.
-
E.
Chambéry
Chambéry is a historic city in southeastern France that served as the political and cultural center of the former Duchy of Savoy.
- 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa2894d0819095704449ecc2db6c |
completed | April 6, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de54d83e44819097abf6714bd48680 |
completed | April 14, 2026, 2:53 p.m. |
Created at: April 6, 2026, 11:04 a.m.