Triple
T16246086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cinsault |
E394373
|
entity |
| Predicate | isAssociatedWithWineDescriptor |
P16142
|
FINISHED |
| Object | soft tannins |
—
|
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: soft tannins | Statement: [Cinsault, isAssociatedWithWineDescriptor, soft tannins]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAssociatedWithWineDescriptor Context triple: [Cinsault, isAssociatedWithWineDescriptor, soft tannins]
-
A.
belongsToAppellation
Indicates that something is associated with or falls under a specific appellation or designated name/category.
-
B.
isFrenchWine
Indicates that the subject is a wine produced in France or recognized as originating from a French wine region.
-
C.
wineStylesAssociatedWith
Indicates a relationship where certain wine styles are linked or connected to a particular entity, such as a region, grape, producer, or product.
-
D.
hasWineDenomination
Indicates that a wine is classified under a specific official denomination or appellation.
-
E.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e24561d250819096f709ea8751fcb9 |
completed | April 17, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69e219ee6f6481909663b388dc99770a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:04 a.m.