Triple
T4314767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lac de Montbel |
E94160
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Lavelanet |
E97295
|
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: Lavelanet | Statement: [Lac de Montbel, locatedNear, Lavelanet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lavelanet Context triple: [Lac de Montbel, locatedNear, Lavelanet]
-
A.
Lavelanet
chosen
Lavelanet is a small town in southwestern France known for its textile industry heritage and location in the foothills of the Pyrenees.
-
B.
Valleiry
Valleiry is a small French commune in the Haute-Savoie department of the Auvergne-Rhône-Alpes region in southeastern France, near the Swiss border.
-
C.
La Baille
La Baille is the traditional nickname for the French Naval Academy, the institution responsible for training officers of the French Navy.
-
D.
Louvois
Louvois was a powerful French statesman, best known as Louis XIV’s influential war minister who significantly shaped France’s military administration in the late 17th century.
-
E.
Lalumière
Lalumière is a French surname most notably borne by Catherine Lalumière, a prominent French politician and former European Parliament member.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350f4448481908be2c7df9cc71bb9 |
completed | March 12, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d08245408190b1ce584c636bf168 |
completed | March 14, 2026, 9:17 p.m. |
Created at: March 12, 2026, 11:12 p.m.