Triple
T24367194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canaiolo Nero |
E614227
|
entity |
| Predicate | wineRole |
P57084
|
FINISHED |
| Object | softening component 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: softening component in blends | Statement: [Canaiolo Nero, wineRole, softening component in blends]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineRole Context triple: [Canaiolo Nero, wineRole, softening component in blends]
-
A.
roleInCognac
Indicates that an entity plays a specific role or function within the context of cognac (e.g., its production, classification, or related processes).
-
B.
wineBlendRole
chosen
Indicates the specific role or function that a wine plays within a blend (e.g., primary component, supporting component, or minor addition).
-
C.
wineEconomyRole
Indicates the role or function an entity has within the wine-related economy, such as production, distribution, trade, or regulation.
-
D.
wineComponent
Indicates that one entity is a constituent ingredient or part of a wine represented by the other entity.
-
E.
wineRegionRole
Indicates the specific role or function that a wine-producing region has in relation to wine production, classification, or designation.
- 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_69e2d7e1e010819098b95eb3f905943d |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f29388c9308190a7a70bf75ed1501c |
completed | April 29, 2026, 11:26 p.m. |
| PD | Predicate disambiguation | batch_69f287bb1b2c81909c2e7fcc392ad143 |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 2:01 a.m.