Triple
T9521238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Bouscaut |
E229647
|
entity |
| Predicate | grapeProportionWhite |
P67384
|
FINISHED |
| Object | Sémillon-dominant blend |
—
|
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: Sémillon-dominant blend | Statement: [Château Bouscaut, grapeProportionWhite, Sémillon-dominant blend]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grapeProportionWhite Context triple: [Château Bouscaut, grapeProportionWhite, Sémillon-dominant blend]
-
A.
grapeColorDistribution
chosen
Indicates how colors are proportionally or categorically distributed among a set of grapes.
-
B.
grapeColorProduced
Indicates the color that is produced by or characteristic of a given grape.
-
C.
grapeSource
Indicates that one entity is the origin or provider of grapes used by another entity.
-
D.
whiteWineShare
Indicates the proportion or share of white wine within a larger set, such as total wine consumption, production, or sales.
-
E.
grapeColorForReds
Indicates that the predicate specifies the typical color of grapes used to produce red wines.
- 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_69ca847870a881909d8d751a7d29da39 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9884dd5c8190b69c178cb2ac75c2 |
completed | April 1, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cca56a3d088190bdc16670678fb6c6 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:59 p.m.