Triple
T34570079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Auxerrois blanc |
E887600
|
entity |
| Predicate | majorCultivationCountry |
P178003
|
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: [Auxerrois blanc, majorCultivationCountry, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorCultivationCountry Context triple: [Auxerrois blanc, majorCultivationCountry, France]
-
A.
majorPlantCountry
chosen
Indicates that a country is a primary or significant producer or cultivator of a particular plant species.
-
B.
cultivatedInCountry
Indicates that something (such as a crop, plant, or organism) is grown or produced through cultivation within the specified country.
-
C.
majorGrowingCountry
Indicates that a country is experiencing significant economic or developmental growth and is considered one of the leading emerging nations.
-
D.
cultivatedPrimarilyIn
Indicates that something is mainly grown, produced, or farmed in a particular place or environment.
-
E.
widelyCultivatedIn
Indicates that something is grown extensively or on a large scale within a particular place or region.
- 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_69f349d1a5fc81908557a46875b2f157 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff56ef0a5c8190ae729d66a8cf7fc4 |
completed | May 9, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69ff539859c481909ec56310da418688 |
completed | May 9, 2026, 3:32 p.m. |
Created at: May 1, 2026, 2:02 a.m.