Triple
T23102683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dôle Blanche (rosé style) |
E576074
|
entity |
| Predicate | typicalGrapeBlend |
P103849
|
FINISHED |
| Object | Pinot Noir and Gamay |
—
|
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: Pinot Noir and Gamay | Statement: [Dôle Blanche (rosé style), typicalGrapeBlend, Pinot Noir and Gamay]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalGrapeBlend Context triple: [Dôle Blanche (rosé style), typicalGrapeBlend, Pinot Noir and Gamay]
-
A.
typicalBlendMerlotPercentage
Indicates the usual proportion of Merlot used in a blend relative to the other grape varieties.
-
B.
typicalBlendCabernetFrancPercentage
Indicates the percentage of Cabernet Franc that is typically included in a particular wine blend.
-
C.
typicalBlendProportionCabernetSauvignon
Indicates the usual proportion of Cabernet Sauvignon used in a wine blend relative to the other grape varieties.
-
D.
grapeBlendPartner
chosen
Indicates that two grape varieties are commonly combined or well-suited to be blended together in winemaking.
-
E.
grapeVarietyType
Indicates the specific type or classification of a grape variety used or referred to in a given context.
- 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de9fa8c81909fd26ff37173b85b |
completed | April 29, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
Created at: April 17, 2026, 3:58 p.m.