Triple
T637650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sauvignon Blanc |
E16660
|
entity |
| Predicate | colorOfGrapeSkin |
P12665
|
FINISHED |
| Object | green |
—
|
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: green | Statement: [Sauvignon Blanc, colorOfGrapeSkin, green]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colorOfGrapeSkin Context triple: [Sauvignon Blanc, colorOfGrapeSkin, green]
-
A.
colorOfBerrySkin
chosen
Indicates the specific color that characterizes the outer skin of a berry.
-
B.
wineColor
Indicates the color attribute or hue associated with a given wine.
-
C.
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).
-
D.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
E.
usesGrapeType
Indicates that one entity employs or incorporates a specific type or variety of grape in its composition, production, or process.
- 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.