Triple
T34535104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Priorat DOQ |
E886645
|
entity |
| Predicate | vineyardCharacteristic |
P42963
|
FINISHED |
| Object | low yields |
—
|
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: low yields | Statement: [Priorat DOQ, vineyardCharacteristic, low yields]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vineyardCharacteristic Context triple: [Priorat DOQ, vineyardCharacteristic, low yields]
-
A.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
B.
viticulturalFeature
chosen
Indicates a characteristic, condition, or attribute specifically related to grape growing or vineyard cultivation.
-
C.
wineCharacteristic
Indicates a descriptive property or quality attributed to a wine, such as its flavor, aroma, color, or style.
-
D.
vineyardName
Indicates the specific name assigned to a vineyard in the relationship.
-
E.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
- 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_69f349ce5eb881909e431c670944aa68 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71feb9138819081afa69a2e2aebf3 |
completed | May 3, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69f71cc8074c81909ae09bea2acf1a09 |
completed | May 3, 2026, 10 a.m. |
Created at: May 1, 2026, 2:02 a.m.