Triple
T33624782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clairette blanche |
E861372
|
entity |
| Predicate | cultivatedCountry |
P56227
|
FINISHED |
| Object | France |
—
|
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: France | Statement: [Clairette blanche, cultivatedCountry, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cultivatedCountry Context triple: [Clairette blanche, cultivatedCountry, France]
-
A.
cultivatedInCountry
chosen
Indicates that something (such as a crop, plant, or organism) is grown or produced through cultivation within the specified country.
-
B.
widelyCultivatedIn
Indicates that something is grown extensively or on a large scale within a particular place or region.
-
C.
cultivatedInContinent
Indicates that something (typically a crop or organism) is grown or cultivated within the geographic boundaries of a specified continent.
-
D.
cultivatedPrimarilyIn
Indicates that something is mainly grown, produced, or farmed in a particular place or environment.
-
E.
cultivatedFor
Indicates that one entity is intentionally grown, raised, or developed for the benefit, use, or purpose of 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_69f34980fabc81909819228729a9ca84 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f822a0248190bf85849636eaff6d |
completed | May 3, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:41 a.m.