Triple
T4984093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pont d’Arc |
E111957
|
entity |
| Predicate | closestMajorCity |
P1982
|
FINISHED |
| Object | Montélimar |
E285655
|
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: Montélimar | Statement: [Pont d’Arc, closestMajorCity, Montélimar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montélimar Context triple: [Pont d’Arc, closestMajorCity, Montélimar]
-
A.
Montélimar
chosen
Montélimar is a town in southeastern France, known as the "gateway to Provence" and famous for its traditional nougat confectionery.
-
B.
Épinal
Épinal is a historic town in northeastern France, known for its traditional image-printing industry and picturesque setting in the Vosges region.
-
C.
Brioude
Brioude is a historic town in south-central France known for its Romanesque Basilica of Saint-Julien and its location in the Haute-Loire department of the Auvergne region.
-
D.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
E.
Belfort
Belfort is the surname of Jordan Belfort, the American former stockbroker, motivational speaker, and author whose high-profile fraud case inspired the film "The Wolf of Wall Street."
- 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_69bd441adc208190b70a033a0741d01e |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7255d7b4819098b537df5b1a4c3c |
completed | March 20, 2026, 4:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf832e0da88190aa6b09dd88fb6157 |
completed | March 22, 2026, 5:50 a.m. |
Created at: March 20, 2026, 1:33 p.m.