Triple
T12373413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piña Colada |
E295058
|
entity |
| Predicate | typicalGlassVolume |
P98818
|
FINISHED |
| Object | approximately 300–400 ml |
—
|
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: approximately 300–400 ml | Statement: [Piña Colada, typicalGlassVolume, approximately 300–400 ml]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalGlassVolume Context triple: [Piña Colada, typicalGlassVolume, approximately 300–400 ml]
-
A.
typeOfCup
Indicates the specific kind or category of cup that an entity is associated with or classified as.
-
B.
glassType
Indicates the specific kind or category of glass associated with or used by an entity.
-
C.
volumeOf
chosen
Indicates the quantitative three-dimensional space occupied by an entity or contained within an object.
-
D.
servingSizeAtOktoberfest
Indicates the typical quantity or portion in which something (such as food or drink) is served specifically at Oktoberfest.
-
E.
bottlingStrengthRange
Indicates the range of alcohol strengths at which a beverage is bottled.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d942a2d6e08190a13c7ff89af09354 |
completed | April 10, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69d93ecf6b548190a394b6b56a0c1c68 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:54 p.m.