Triple
T6916586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bresse |
E160069
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Bugey |
E280236
|
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: Bugey | Statement: [Bresse, borderedBy, Bugey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bugey Context triple: [Bresse, borderedBy, Bugey]
-
A.
Bugey
chosen
Bugey is a historical and wine-producing region in eastern France, known for its hilly landscapes and location in the foothills of the Jura Mountains.
-
B.
Araria
Araria is a town and administrative headquarters of Araria district in the northeastern part of the Indian state of Bihar, near the border with Nepal.
-
C.
Miyama
Miyama is a Japanese municipality known for its traditional rural landscapes and cultural heritage.
-
D.
Yokote
Yokote is a city in Akita Prefecture, Japan, known for its heavy snowfall and the annual Yokote Kamakura Snow Festival featuring traditional igloo-like snow huts.
-
E.
Yabu
Yabu is a small city in northern Hyōgo Prefecture, Japan, known for its rural landscapes, hot springs, and access to mountainous outdoor recreation.
- 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_69c6883ab1008190a07129ff06f625d9 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d9e034cc81908f1e8f31b055e119 |
completed | March 27, 2026, 7:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7512b39b081908370c43ed3d65829 |
completed | March 28, 2026, 3:55 a.m. |
Created at: March 27, 2026, 2:26 p.m.