Triple
T6707580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vino Nobile di Montepulciano |
E153046
|
entity |
| Predicate | typicalBlendShareOfOtherGrapes |
P44611
|
FINISHED |
| Object | maximum 30% |
—
|
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: maximum 30% | Statement: [Vino Nobile di Montepulciano, typicalBlendShareOfOtherGrapes, maximum 30%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalBlendShareOfOtherGrapes Context triple: [Vino Nobile di Montepulciano, typicalBlendShareOfOtherGrapes, maximum 30%]
-
A.
typicalViognierPercentage
Indicates the usual proportion of Viognier used within a given wine, blend, or production context.
-
B.
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).
-
C.
traditionalGrapeVariety
Indicates that a grape variety is traditionally or historically used in a specific region, wine style, or cultural winemaking practice.
-
D.
typicalBlendShare
chosen
Indicates the usual proportion or percentage that one component contributes to a blend relative to the other components.
-
E.
maximumViognierPercentage
Indicates the highest allowable proportion of Viognier in a given wine or blend.
- 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_69c68808d8d8819087369015270788fe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:06 p.m.