Triple
T31723397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Cheval Blanc |
E809643
|
entity |
| Predicate | hasWineryBuilding |
P45101
|
FINISHED |
| Object | modern gravity-flow winery |
—
|
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: modern gravity-flow winery | Statement: [Château Cheval Blanc, hasWineryBuilding, modern gravity-flow winery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWineryBuilding Context triple: [Château Cheval Blanc, hasWineryBuilding, modern gravity-flow winery]
-
A.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
B.
hasWinemakingFacility
chosen
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
C.
hasNearbyWinery
Indicates that one entity is located close to, or in the vicinity of, a winery.
-
D.
hasWineryType
Indicates the specific category or classification of winery associated with an entity.
-
E.
hasWineInstitution
Indicates that an entity is associated with, managed by, or belongs to a specific wine-related institution (such as a winery, wine school, or wine organization).
- 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_69f348e009c8819095d77df52c645b9c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 30, 2026, 11:19 p.m.