Triple
T19341874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verduzzo Friulano |
E483773
|
entity |
| Predicate | wineTanninSource |
P135483
|
FINISHED |
| Object | grape skins |
—
|
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: grape skins | Statement: [Verduzzo Friulano, wineTanninSource, grape skins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineTanninSource Context triple: [Verduzzo Friulano, wineTanninSource, grape skins]
-
A.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
B.
grapeSource
Indicates that one entity is the origin or provider of grapes used by another 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.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
E.
wineStyleContribution
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
- 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e6185795bc8190a63061ca794c0d67 |
completed | April 20, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69e4dd12303c8190a2027c062b2dff40 |
completed | April 19, 2026, 1:48 p.m. |
| PDg | Predicate description generation | batch_69e4df51ac6c819091ce72b07790ffa6 |
completed | April 19, 2026, 1:57 p.m. |
Created at: April 10, 2026, 1:33 p.m.