Triple
T16174297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Négrette must represent a significant proportion of blends |
E392521
|
entity |
| Predicate | relatesToWineStyle |
P55208
|
FINISHED |
| Object | red wine |
—
|
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: red wine | Statement: [Négrette must represent a significant proportion of blends, relatesToWineStyle, red wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatesToWineStyle Context triple: [Négrette must represent a significant proportion of blends, relatesToWineStyle, red wine]
-
A.
wineStylesAssociatedWith
chosen
Indicates a relationship where certain wine styles are linked or connected to a particular entity, such as a region, grape, producer, or product.
-
B.
wineStyleContribution
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
-
C.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
D.
wineStyleComparedTo
Indicates a comparison between wines in terms of their style or stylistic characteristics.
-
E.
wineVariety
Indicates the specific type or variety of wine associated with an entity.
- 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_69d87f1d32208190942e4e499a80c18c |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21eb9b8208190b60874cec7a3a98e |
completed | April 17, 2026, 11:51 a.m. |
| PD | Predicate disambiguation | batch_69e219d642708190ba31a90dce76a210 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.