Triple
T3099796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valle de Guadalupe wine region |
E64686
|
entity |
| Predicate | approxNumberOfWineries |
P45927
|
FINISHED |
| Object | over 100 wineries |
—
|
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: over 100 wineries | Statement: [Valle de Guadalupe wine region, approxNumberOfWineries, over 100 wineries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approxNumberOfWineries Context triple: [Valle de Guadalupe wine region, approxNumberOfWineries, over 100 wineries]
-
A.
hasWinery
Indicates a relationship where a subject owns, operates, or is associated with a particular winery.
-
B.
vineyardSize
Indicates the extent or area of land occupied by a vineyard.
-
C.
hasWinemakingFacility
Indicates that an entity possesses or is associated with a facility where winemaking activities are carried out.
-
D.
producesWine
Indicates that one entity creates or manufactures wine as a product.
-
E.
numberOfBreweries
Indicates the count of breweries associated with a given entity or within a specified context.
- 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_69ad857dc98481909e585dc3372e3ed5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada269a9188190aada5b3799d4dfd7 |
completed | March 8, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69ad9df06ed88190809f0683122caa5a |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada0f6fef48190b13898be383a246b |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 8, 2026, 3:03 p.m.