Triple
T33752694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château d’Issan |
E864887
|
entity |
| Predicate | hasMainWine |
P89315
|
FINISHED |
| Object | Château d’Issan Grand Vin |
—
|
NE NERFINISHED |
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: Château d’Issan Grand Vin | Statement: [Château d’Issan, hasMainWine, Château d’Issan Grand Vin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainWine Context triple: [Château d’Issan, hasMainWine, Château d’Issan Grand Vin]
-
A.
mainWine
chosen
Indicates that one wine is the primary or featured wine associated with a given context, event, or entity.
-
B.
hasWineCategory
Indicates that one entity is classified under, or associated with, a particular category or type of wine.
-
C.
hasSparklingWine
Indicates that an entity possesses, includes, or is associated with sparkling wine.
-
D.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
E.
hasWinemakingOption
Indicates that an entity offers or includes a particular method, feature, or configuration related to winemaking.
- 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_69f3498c35f881909df279ae4270f831 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a013a251b4081908b43d85dd95586c5 |
completed | May 11, 2026, 2:08 a.m. |
| PD | Predicate disambiguation | batch_6a0137e4ea988190812173a5ff044098 |
completed | May 11, 2026, 1:59 a.m. |
Created at: May 1, 2026, 1:45 a.m.