Triple
T637724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sauvignon Blanc |
E16660
|
entity |
| Predicate | wineAcidityDescriptor |
P16142
|
FINISHED |
| Object | zesty |
—
|
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: zesty | Statement: [Sauvignon Blanc, wineAcidityDescriptor, zesty]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineAcidityDescriptor Context triple: [Sauvignon Blanc, wineAcidityDescriptor, zesty]
-
A.
wineCharacteristic
chosen
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
B.
acidityLevel
Indicates the degree or intensity of acidity associated with an entity, typically measured by pH or a comparable scale.
-
C.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
D.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
E.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
- 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_69a4936be1c88190af56540324b57da7 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a17b125481909a6ab53424954792 |
completed | March 1, 2026, 8:28 p.m. |
| PD | Predicate disambiguation | batch_69a49d0629308190bcc137639567f7c2 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.