Triple
T17253716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aravis ski area |
E418825
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Haute-Savoie |
E16739
|
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: Haute-Savoie | Statement: [Aravis ski area, region, Haute-Savoie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haute-Savoie Context triple: [Aravis ski area, region, Haute-Savoie]
-
A.
Haute-Savoie
chosen
Haute-Savoie is a department in the Auvergne-Rhône-Alpes region of southeastern France, renowned for its Alpine landscapes, ski resorts, and proximity to Mont Blanc and the Swiss and Italian borders.
-
B.
Savoie
Savoie is a mountainous department in southeastern France, known for its Alpine landscapes, ski resorts, and rich Savoyard cultural heritage.
-
C.
Hautes-Alpes
Hautes-Alpes is a mountainous department in southeastern France known for its Alpine landscapes, ski resorts, and outdoor recreation.
-
D.
Montgenèvre
Montgenèvre is a French Alpine ski resort village in the Hautes-Alpes department, known for its high-altitude slopes and location near the Italian border.
-
E.
Gros-de-Vaud
Gros-de-Vaud is a predominantly rural district in the canton of Vaud, Switzerland, known for its agricultural landscape and small towns.
- 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_69d886d9ab108190b70edd8d17aa1204 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42e6b2b1c8190b446ce648ecaad80 |
completed | April 19, 2026, 1:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180cc1da88190b91cbcd3565528fc |
completed | May 11, 2026, 7:10 a.m. |
Created at: April 10, 2026, 5:39 a.m.