Triple
T28034011
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spätburgunder |
E708351
|
entity |
| Predicate | hasTypicalTanninLevel |
P2069
|
FINISHED |
| Object | low tannin |
—
|
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: low tannin | Statement: [Spätburgunder, hasTypicalTanninLevel, low tannin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalTanninLevel Context triple: [Spätburgunder, hasTypicalTanninLevel, low tannin]
-
A.
tanninLevel
chosen
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
B.
hasBitternessLevel
Indicates that an entity is associated with a specific degree or intensity of bitterness.
-
C.
allowsTastingOf
Indicates that one entity permits another entity to sample or try the taste of something.
-
D.
typicalBlendMerlotPercentage
Indicates the usual proportion of Merlot used in a blend relative to the other grape varieties.
-
E.
typicalBlendCabernetFrancPercentage
Indicates the percentage of Cabernet Franc that is typically included in a particular wine blend.
- 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_69ef9b6bdd9c8190bb3a574a03774ad1 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69fccdd496048190bca801a8a9eecb62 |
completed | May 7, 2026, 5:37 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
Created at: April 27, 2026, 8:19 p.m.