Triple
T12117087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gamay Noir |
E288592
|
entity |
| Predicate | wineProfileSummary |
P20482
|
FINISHED |
| Object | light-bodied, high-acid, low-tannin, fruit-driven red 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: light-bodied, high-acid, low-tannin, fruit-driven red wines | Statement: [Gamay Noir, wineProfileSummary, light-bodied, high-acid, low-tannin, fruit-driven red wines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineProfileSummary Context triple: [Gamay Noir, wineProfileSummary, light-bodied, high-acid, low-tannin, fruit-driven red wines]
-
A.
wineStructure
chosen
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
-
B.
wineReputation
Indicates the perceived quality, prestige, or standing of a wine based on expert opinion, consumer perception, or historical recognition.
-
C.
wineName
Indicates the specific name or designation assigned to a wine.
-
D.
wineStyleContribution
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
-
E.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.