Triple
T14606906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Torrontés |
E342852
|
entity |
| Predicate | wineSweetnessStyle |
P62205
|
FINISHED |
| Object | usually dry |
—
|
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: usually dry | Statement: [Torrontés, wineSweetnessStyle, usually dry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineSweetnessStyle Context triple: [Torrontés, wineSweetnessStyle, usually dry]
-
A.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
B.
wineStyleContribution
Indicates how much a given factor or component influences or shapes the overall style or character of a wine.
-
C.
wineAcidityType
Indicates the type or category of acidity associated with a given wine.
-
D.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
E.
typicalSweetnessLevel
chosen
Indicates the usual or characteristic degree of sweetness associated with something.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44d327c8190a8d20568429d0f80 |
completed | April 14, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69de656f9f4c81909f815b6629a9ee39 |
completed | April 14, 2026, 4:03 p.m. |
Created at: April 10, 2026, 1:25 a.m.