Triple
T8689933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dão wine region |
E206259
|
entity |
| Predicate | averageVineyardAltitude |
P84207
|
FINISHED |
| Object | 400–700 meters |
—
|
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: 400–700 meters | Statement: [Dão wine region, averageVineyardAltitude, 400–700 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageVineyardAltitude Context triple: [Dão wine region, averageVineyardAltitude, 400–700 meters]
-
A.
vineyardAreaRankInFrance
Indicates the relative position of an entity’s vineyard area compared to other vineyard areas within France, ordered by size.
-
B.
vineyardLocation
Indicates the geographical place where a vineyard is situated or established.
-
C.
vineyardSize
Indicates the extent or area of land occupied by a vineyard.
-
D.
viticulturalCharacteristic
Indicates a relationship where a specific trait, quality, or property is attributed to viticulture or grape-growing practices.
-
E.
hasVineyards
Indicates that one entity possesses, contains, or is associated with vineyards used for growing grapevines.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5734602c81909a0687e00f4a4a26 |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc4569f9048190b9c86b4c81103d35 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc483f06f48190879f4702c8b4ed00 |
completed | March 31, 2026, 10:18 p.m. |
Created at: March 30, 2026, 6:33 p.m.