Triple
T16328474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bourboulenc |
E396484
|
entity |
| Predicate | wineAppellationUsage |
P68485
|
FINISHED |
| Object | Côtes du Rhône white wines |
—
|
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: Côtes du Rhône white wines | Statement: [Bourboulenc, wineAppellationUsage, Côtes du Rhône white wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineAppellationUsage Context triple: [Bourboulenc, wineAppellationUsage, Côtes du Rhône white wines]
-
A.
wineAppellation
chosen
Indicates that a wine originates from, and is classified under, a specific geographic appellation or designated wine-producing region.
-
B.
wineLawRegionName
Indicates that a specific name refers to the legal wine-producing region defined by wine regulations.
-
C.
appellationType
Indicates the specific kind or category of name or designation applied to an entity.
-
D.
wineStyleOrigin
Indicates that a particular wine style originated in or is traditionally associated with a specific geographic region or place.
-
E.
wineRegionCategory
Indicates a classification relationship where a wine region is assigned to a specific category or type of wine-producing area.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2c4ddc5608190b24fe2e871691470 |
completed | April 17, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69e226eba9b48190af6e80d3d1c2aed3 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:07 a.m.