Triple
T2328843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Entre-Deux-Mers |
E48353
|
entity |
| Predicate | productionFocus |
P7216
|
FINISHED |
| Object | volume white wine production |
—
|
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: volume white wine production | Statement: [Entre-Deux-Mers, productionFocus, volume white wine production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: productionFocus Context triple: [Entre-Deux-Mers, productionFocus, volume white wine production]
-
A.
produces
Indicates that one entity creates, generates, or yields another entity as a result or output.
-
B.
increasesProductionOf
Indicates that one entity causes a rise or enhancement in the amount or rate at which another entity is produced.
-
C.
majorProductionHub
Indicates that a location serves as a primary center where large-scale production or manufacturing activities are concentrated.
-
D.
primaryProduct
chosen
Indicates that one entity is the main or most important product associated with, produced by, or offered by another entity.
-
E.
programFocus
Indicates that an educational or training program is primarily oriented around or concentrated on a particular subject, theme, or objective.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abcc30c5e881908c5d526d7e7491d0 |
completed | March 7, 2026, 6:56 a.m. |
| PD | Predicate disambiguation | batch_69abc5926d048190a535e3f23d41de2a |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:50 p.m.