Triple
T19341793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ribolla Gialla |
E483771
|
entity |
| Predicate | tanninPresenceInOrangeWines |
P2069
|
FINISHED |
| Object | noticeable |
—
|
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: noticeable | Statement: [Ribolla Gialla, tanninPresenceInOrangeWines, noticeable]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tanninPresenceInOrangeWines Context triple: [Ribolla Gialla, tanninPresenceInOrangeWines, noticeable]
-
A.
tanninLevel
chosen
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
B.
usesWineType
Indicates that one entity makes use of, incorporates, or is associated with a particular type or category of wine.
-
C.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
D.
oenologicalSignificance
Indicates the relationship in which something holds importance, relevance, or notable impact within the context of wine or winemaking.
-
E.
vinificationUse
Indicates the process or method of winemaking applied to a given wine or batch.
- 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_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. |
Created at: April 10, 2026, 1:33 p.m.