Triple
T20134931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pays arpitan |
E491000
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Bresse |
—
|
NE NERFINISHED |
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: Bresse | Statement: [Pays arpitan, contains, Bresse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bresse Context triple: [Pays arpitan, contains, Bresse]
-
A.
Bresse
chosen
Bresse is a historical region in eastern France known for its rich agricultural land, distinctive culinary traditions, and cultural ties to the Franco-Provençal linguistic area.
-
B.
Cuiseaux
Cuiseaux is a small commune in eastern France, notable as the birthplace of the painter Édouard Vuillard.
-
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.
Sevrier
Sevrier is a lakeside commune in southeastern France situated on the western shore of Lake Annecy in the Haute-Savoie department.
-
E.
Levensoises
Levensoises are the female inhabitants or natives of Levens, a commune in the Alpes-Maritimes department in southeastern France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66766e46c81908721fd47066dc9f8 |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.