Triple
T4914105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serre-Ponçon Dam |
E110305
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Embrun |
E491876
|
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: Embrun | Statement: [Serre-Ponçon Dam, locatedNear, Embrun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Embrun Context triple: [Serre-Ponçon Dam, locatedNear, Embrun]
-
A.
Embrun
Embrun is a rapidly growing Franco-Ontarian community in eastern Ontario, known for its bilingual character and proximity to Ottawa.
-
B.
Embrun
chosen
Embrun is a historic town in southeastern France’s Hautes-Alpes department, known for its picturesque setting in the Alps and proximity to the Lac de Serre-Ponçon.
-
C.
Nyons
Nyons is a small town in southeastern France renowned for its olive production and picturesque setting in the Drôme Provençale region.
-
D.
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
E.
Voiron
Voiron is a commune in southeastern France known for its historical town center and proximity to the Chartreuse Mountains.
- 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_69bd44132b94819088522d92beaadc78 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e9f12b48190b3cb5378958d03cd |
completed | March 20, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf186311c88190a8fe34e497e4662b |
completed | March 21, 2026, 10:14 p.m. |
Created at: March 20, 2026, 1:29 p.m.