Triple
T31222765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Domaine Clarence Dillon |
E796058
|
entity |
| Predicate | hasWineShop |
P176038
|
FINISHED |
| Object | La Cave du Château |
—
|
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: La Cave du Château | Statement: [Domaine Clarence Dillon, hasWineShop, La Cave du Château]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWineShop Context triple: [Domaine Clarence Dillon, hasWineShop, La Cave du Château]
-
A.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
B.
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).
-
C.
hasWineVillage
Indicates that a place or region includes or is associated with a village known for wine production or viticulture.
-
D.
hasNearbyWinery
Indicates that one entity is located close to, or in the vicinity of, a winery.
-
E.
hasWineCategory
Indicates that one entity is classified under, or associated with, a particular category or type of wine.
- F. None of above. chosen
Provenance (4 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_69f224da98f88190ab32f690cce5d303 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6db3606808190a80c6e9f5da5b33e |
completed | May 3, 2026, 5:20 a.m. |
| PD | Predicate disambiguation | batch_69f6d82adfa481908a5e196d2e18c73f |
completed | May 3, 2026, 5:07 a.m. |
| PDg | Predicate description generation | batch_69f6db1bc348819097c844f76e2fa4fe |
completed | May 3, 2026, 5:20 a.m. |
Created at: April 29, 2026, 9:10 p.m.