Triple
T30173040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haut-Montravel AOC |
E766972
|
entity |
| Predicate | grapeBlend |
P103849
|
FINISHED |
| Object | Sémillon-dominant blends |
—
|
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: Sémillon-dominant blends | Statement: [Haut-Montravel AOC, grapeBlend, Sémillon-dominant blends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeBlend Context triple: [Haut-Montravel AOC, grapeBlend, Sémillon-dominant blends]
-
A.
grapeBlendPartner
chosen
Indicates that two grape varieties are commonly combined or well-suited to be blended together in winemaking.
-
B.
wineBlendRole
Indicates the specific role or function that a wine plays within a blend (e.g., primary component, supporting component, or minor addition).
-
C.
typicalBlendMerlotPercentage
Indicates the usual proportion of Merlot used in a blend relative to the other grape varieties.
-
D.
typicalBlendCabernetFrancPercentage
Indicates the percentage of Cabernet Franc that is typically included in a particular wine blend.
-
E.
grapeVarietal
Indicates that one entity is a specific type or variety of grape used in wine or grape production in relation to another entity.
- 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_69f2247ba20c81909d34f2bfed706e1e |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67f0ce128819087de473561c2fd51 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f678ce54b081908c26edfd49e39c60 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 7:25 p.m.