Triple
T31704053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deuxième Cru Classé |
E809132
|
entity |
| Predicate | hasNotableSweetWineArea |
P51239
|
FINISHED |
| Object | Sauternes |
—
|
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: Sauternes | Statement: [Deuxième Cru Classé, hasNotableSweetWineArea, Sauternes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableSweetWineArea Context triple: [Deuxième Cru Classé, hasNotableSweetWineArea, Sauternes]
-
A.
sweetWineArea
chosen
Indicates an area or region that is known for producing or associated with sweet wine.
-
B.
isInWineOrBeerRegion
Indicates that something is located within a geographic area known for producing wine or beer.
-
C.
isWineProducingTown
Indicates that a town is known for producing wine, typically through local vineyards or wineries.
-
D.
alsoProducesWineIn
Indicates that the subject, in addition to other products or activities, produces wine in the specified location or context.
-
E.
notableSparklingRegion
Indicates that a region is particularly recognized or distinguished for producing sparkling wine.
- 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_69f348de914081909fc8edff56f34dbe |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a00b7fb90f881908f73edf2be8cc3a5 |
completed | May 10, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_6a00b75593d08190b3e76191cd79cdec |
completed | May 10, 2026, 4:50 p.m. |
Created at: April 30, 2026, 11:13 p.m.