Triple
T19514641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Côte Blonde |
E488245
|
entity |
| Predicate | typicalViognierProportion |
P61433
|
FINISHED |
| Object | small percentage of the blend |
—
|
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: small percentage of the blend | Statement: [Côte Blonde, typicalViognierProportion, small percentage of the blend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalViognierProportion Context triple: [Côte Blonde, typicalViognierProportion, small percentage of the blend]
-
A.
typicalViognierPercentage
chosen
Indicates the usual proportion of Viognier used within a given wine, blend, or production context.
-
B.
maximumViognierPercentage
Indicates the highest allowable proportion of Viognier in a given wine or blend.
-
C.
typicalBlendCabernetFrancPercentage
Indicates the percentage of Cabernet Franc that is typically included in a particular wine blend.
-
D.
typicalBlendMerlotPercentage
Indicates the usual proportion of Merlot used in a blend relative to the other grape varieties.
-
E.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
- 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359a7070819099d925447c80bf23 |
completed | April 20, 2026, 2:18 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7bd25881908caa04eaef1f6718 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:40 p.m.