Triple
T2853597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tvishi |
E63147
|
entity |
| Predicate | wineColorCategory |
P12088
|
FINISHED |
| Object | white wine |
—
|
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 wine | Statement: [Tvishi, wineColorCategory, white wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wineColorCategory Context triple: [Tvishi, wineColorCategory, white wine]
-
A.
wineColor
chosen
Indicates the color attribute or hue associated with a given wine.
-
B.
wineCategory
Indicates the classification or type of wine that an entity (such as a specific wine) belongs to.
-
C.
wineStyle
Indicates the stylistic category or type of wine (such as its production style, sweetness, body, or other defining characteristics) associated with an entity.
-
D.
grapeColorProduced
Indicates the color that is produced by or characteristic of a given grape.
-
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_69ab4c407c408190857d25e027155ce9 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf5f21348190a574fa86bc71c76f |
completed | March 7, 2026, 8:18 a.m. |
| PD | Predicate disambiguation | batch_69abdd10aef88190b750aae07e7df4dc |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.