Triple
T35516185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caiño Blanco |
E1026424
|
entity |
| Predicate | wineProfileUse |
P183387
|
FINISHED |
| Object | used to add acidity in 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: used to add acidity in blends | Statement: [Caiño Blanco, wineProfileUse, used to add acidity in blends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineProfileUse Context triple: [Caiño Blanco, wineProfileUse, used to add acidity in blends]
-
A.
wineStyleContribution
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
-
B.
wineComponent
Indicates that one entity is a constituent ingredient or part of a wine represented by the other entity.
-
C.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
-
D.
usesWineType
Indicates that one entity makes use of, incorporates, or is associated with a particular type or category of wine.
-
E.
wineStylesAssociatedWith
Indicates a relationship where certain wine styles are linked or connected to a particular entity, such as a region, grape, producer, or product.
- F. None of above. chosen
Provenance (4 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_69f76dfe78b081908e2b14cb88dd8c00 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79f7340e4819092a1a47f7028e63f |
completed | May 3, 2026, 7:18 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
| PDg | Predicate description generation | batch_69f79ec14ce08190b22cee0b40d33743 |
completed | May 3, 2026, 7:15 p.m. |
Created at: May 3, 2026, 4:04 p.m.