Triple
T36746192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Jumilla |
E907770
|
entity |
| Predicate | hasVineyardCharacteristic |
P42963
|
FINISHED |
| Object | bush-trained vines |
—
|
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: bush-trained vines | Statement: [City of Jumilla, hasVineyardCharacteristic, bush-trained vines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVineyardCharacteristic Context triple: [City of Jumilla, hasVineyardCharacteristic, bush-trained vines]
-
A.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
B.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
-
C.
hasVineyardType
Indicates the specific type or classification of a vineyard associated with an entity.
-
D.
viticulturalFeature
chosen
Indicates a characteristic, condition, or attribute specifically related to grape growing or vineyard cultivation.
-
E.
hasVineyardsNear
Indicates that one entity possesses or is associated with vineyards located in close geographic proximity to another 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_69f76e76d10881909ec1679bc043108c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fd783fed9c81909e792702636c4f1f |
completed | May 8, 2026, 5:44 a.m. |
| PD | Predicate disambiguation | batch_69fd7788e63c81909de22fdafcfe41c0 |
completed | May 8, 2026, 5:41 a.m. |
Created at: May 3, 2026, 4:12 p.m.