Triple
T10570304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hohneck |
E249460
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | La Bresse |
E326493
|
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: La Bresse | Statement: [Hohneck, near, La Bresse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: La Bresse Context triple: [Hohneck, near, La Bresse]
-
A.
La Bresse
chosen
La Bresse is a French mountain town in northeastern France known for its ski resort and outdoor activities in the Vosges.
-
B.
Riorges
Riorges is a commune in central France, near Roanne in the Loire department, known for its residential character and local cultural life.
-
C.
Arbois
Arbois is a small French town in the Jura region renowned for its vineyards and distinctive Jura wines, particularly vin jaune.
-
D.
Cottévrard
Cottévrard is a small commune in the Seine-Maritime department of the Normandy region in northern France.
-
E.
La Dôle
La Dôle is a prominent mountain peak in the Jura range of western Switzerland, known for its panoramic views over Lake Geneva and the Alps and for hosting telecommunications and weather facilities near its summit.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5274678148190b7a1afc099628357 |
completed | April 7, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b5025b88190a078f5ad7b9cb3d5 |
completed | April 10, 2026, 9:27 p.m. |
Created at: April 6, 2026, 12:37 p.m.