Triple
T20766499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ugni Blanc |
E511111
|
entity |
| Predicate | typicalAlcoholPotential |
P112051
|
FINISHED |
| Object | moderate |
—
|
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: moderate | Statement: [Ugni Blanc, typicalAlcoholPotential, moderate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAlcoholPotential Context triple: [Ugni Blanc, typicalAlcoholPotential, moderate]
-
A.
alcoholType
Indicates the specific kind or category of alcohol associated with an entity (e.g., beer, wine, spirits).
-
B.
wineAlcoholPotential
chosen
Indicates the potential alcohol content that a wine could reach based on its current sugar level or fermentation stage.
-
C.
madeWithAlcohol
Indicates that something is created, prepared, or produced using alcohol as an ingredient or component.
-
D.
isAlcoholicBeverage
Indicates that a beverage contains alcohol and is classified as an alcoholic drink.
-
E.
alcoholStrengthCategory
Indicates the classification of an alcoholic beverage based on the strength or concentration of its alcohol content.
- 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_69e0b4ca01148190ac018e57e0cab46f |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c24ceab8819094e331c57abe6879 |
completed | April 21, 2026, 12:18 a.m. |
| PD | Predicate disambiguation | batch_69e5c0550ec481908a0877fb2409d983 |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:36 p.m.