Triple
T15351488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Côte |
E367062
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Coppet |
E220712
|
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: Coppet | Statement: [La Côte, hasTown, Coppet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coppet Context triple: [La Côte, hasTown, Coppet]
-
A.
Coppet
chosen
Coppet is a Swiss lakeside town on Lake Geneva in the canton of Vaud, known for its historic château and role as a regional transport hub.
-
B.
Villars-le-Comte
Villars-le-Comte is a small rural municipality in the canton of Vaud in western Switzerland.
-
C.
Cluses
Cluses is a small industrial town in southeastern France known for its precision engineering and watchmaking heritage, located in the Arve Valley of the Haute-Savoie department in the Alps.
-
D.
Nyons
Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
-
E.
Saignelégier
Saignelégier is a municipality in the Swiss canton of Jura known for its rural landscapes, watchmaking heritage, and the annual Marché-Concours horse festival.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e290efc8190b22c95dcd3e5f57f |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3aeb59c8190a39ccb4df7815ed0 |
completed | May 9, 2026, 11:30 p.m. |
Created at: April 10, 2026, 3:17 a.m.