Triple
T1944566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinot Gris |
E42033
|
entity |
| Predicate | typicalWineColor |
P12088
|
FINISHED |
| Object | white |
—
|
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: white | Statement: [Pinot Gris, typicalWineColor, white]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWineColor Context triple: [Pinot Gris, typicalWineColor, white]
-
A.
wineColor
chosen
Indicates the color attribute or hue associated with a given wine.
-
B.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
C.
hasBottleColor
Indicates that an entity is associated with a bottle characterized by a specific color.
-
D.
primaryGrapeVariety
Indicates that one entity is the main or predominant grape variety used in producing the other entity (typically a wine or wine-based product).
-
E.
wineStructure
Indicates the overall sensory framework of a wine, encompassing how its components like acidity, tannin, body, and alcohol are balanced and interact.
- 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_69a8870e08fc8190a319cbf2600db15f |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb32ebae881908f7541301f0198ae |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abaff25a588190bb4cbc8df9fc6d64 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:36 p.m.