Triple
T30074408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franconian wine region |
E764277
|
entity |
| Predicate | hasFamousVineyard |
P25137
|
FINISHED |
| Object | Würzburger Stein |
—
|
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: Würzburger Stein | Statement: [Franconian wine region, hasFamousVineyard, Würzburger Stein]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamousVineyard Context triple: [Franconian wine region, hasFamousVineyard, Würzburger Stein]
-
A.
hasVineyards
chosen
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.
hasVineyardsNear
Indicates that one entity possesses or is associated with vineyards located in close geographic proximity to another entity.
-
D.
hasVineyardType
Indicates the specific type or classification of a vineyard associated with an entity.
-
E.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
- 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_69f22472eee081909791dc372aa766e9 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: April 29, 2026, 7:01 p.m.