Triple
T12389026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Mongie |
E295943
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Bagnères-de-Bigorre |
E152412
|
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: Bagnères-de-Bigorre | Statement: [La Mongie, near, Bagnères-de-Bigorre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bagnères-de-Bigorre Context triple: [La Mongie, near, Bagnères-de-Bigorre]
-
A.
Bagnères-de-Bigorre
chosen
Bagnères-de-Bigorre is a spa and ski resort town in the French Pyrenees, known for its thermal baths and mountain tourism.
-
B.
Argelès-Gazost
Argelès-Gazost is a small spa and tourist town in southwestern France, nestled in the Pyrenees and serving as a gateway to nearby mountain valleys and national parks.
-
C.
Bagnères-de-Luchon
Bagnères-de-Luchon is a spa and ski resort town in the French Pyrenees, known for its thermal baths and mountain tourism.
-
D.
Mazamet
Mazamet is a town in southern France known historically for its wool and leather industries, situated in the Tarn department within the Occitanie region.
-
E.
Tarbes
Tarbes is a historic city in southwestern France, serving as the capital of the Hautes-Pyrénées department at the foot of the Pyrenees.
- 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_69d6ad9e653c8190b1473c860ee53dae |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d93fcf6aa8819080c9a2407a72db2e |
completed | April 10, 2026, 6:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6347816408190904ea71d2a72398f |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:54 p.m.