Triple
T5591559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Côtes du Rhône |
E146888
|
entity |
| Predicate | grapeGrowingArea |
P32878
|
FINISHED |
| Object | both banks of the Rhône River |
—
|
LITERAL 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: both banks of the Rhône River | Statement: [Côtes du Rhône, grapeGrowingArea, both banks of the Rhône River]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeGrowingArea Context triple: [Côtes du Rhône, grapeGrowingArea, both banks of the Rhône River]
-
A.
viticulturalAreaCode
Indicates the designated code that identifies the specific viticultural (wine-producing) area associated with an entity.
-
B.
viticulturalAreaDesignation
chosen
Indicates that a specific geographic area is officially designated or recognized for viticulture (grape growing and wine production).
-
C.
viticulturalAreaType
Indicates the specific type or classification of a viticultural area associated with an entity.
-
D.
wineGrapesCultivated
Indicates that certain grape varieties are grown or cultivated specifically for producing wine.
-
E.
viticulturalFeature
Indicates a characteristic, condition, or attribute specifically related to grape growing or vineyard cultivation.
- F. None of above.
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_69c009036c408190981a8d690b679b67 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020a3365c8190bd223226c0a6969f |
completed | March 22, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69c01b16b9bc8190ab0b945507d90e05 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:38 p.m.