Triple
T1413830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Denbies Wine Estate |
E31865
|
entity |
| Predicate | hasVineyardArea |
P19614
|
FINISHED |
| Object | approximately 265 acres |
—
|
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: approximately 265 acres | Statement: [Denbies Wine Estate, hasVineyardArea, approximately 265 acres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVineyardArea Context triple: [Denbies Wine Estate, hasVineyardArea, approximately 265 acres]
-
A.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
-
B.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
C.
viticulturalAreaCode
Indicates the designated code that identifies the specific viticultural (wine-producing) area associated with an entity.
-
D.
vineyardAreaRankInFrance
Indicates the relative position of an entity’s vineyard area compared to other vineyard areas within France, ordered by size.
-
E.
hasNumberOfAcres
chosen
Indicates the specific quantity of land area, measured in acres, that is associated with an entity.
- 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_69a49919a994819086528951bc224775 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3e476f08190aed1576805c62462 |
completed | March 1, 2026, 10:55 p.m. |
| PD | Predicate disambiguation | batch_69a4bf048b648190ab77d9b45cb4855f |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:59 p.m.