Triple
T10662451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokay (historical name in Australia) |
E251259
|
entity |
| Predicate | typicalConsumptionContext |
P37480
|
FINISHED |
| Object | after-dinner wine |
—
|
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: after-dinner wine | Statement: [Tokay (historical name in Australia), typicalConsumptionContext, after-dinner wine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalConsumptionContext Context triple: [Tokay (historical name in Australia), typicalConsumptionContext, after-dinner wine]
-
A.
typicalConsumptionAge
Indicates the age at which something is most commonly or normally consumed.
-
B.
hasTypicalUseContext
chosen
Indicates that something is commonly or characteristically used within a particular situation, setting, or context.
-
C.
consumptionMethod
Indicates the manner or process by which something is consumed, used up, or ingested.
-
D.
typicalCircumstance
Indicates the usual or commonly occurring situation, condition, or context in which an event, action, or relationship typically takes place.
-
E.
consumes
Indicates that one entity eats, drinks, or otherwise uses up another entity as a resource or nourishment.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6e018d1e881909b8e62682104e842 |
completed | April 8, 2026, 11:09 p.m. |
| PD | Predicate disambiguation | batch_69d6dd8753108190b799ffa0c760526e |
completed | April 8, 2026, 10:58 p.m. |
Created at: April 8, 2026, 9:08 p.m.