Triple
T26741621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Bull Yellow Edition Tropical |
E674274
|
entity |
| Predicate | energyDrinkSegment |
P68535
|
FINISHED |
| Object | flavored energy drink |
—
|
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: flavored energy drink | Statement: [Red Bull Yellow Edition Tropical, energyDrinkSegment, flavored energy drink]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: energyDrinkSegment Context triple: [Red Bull Yellow Edition Tropical, energyDrinkSegment, flavored energy drink]
-
A.
beverageSubcategory
chosen
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
B.
beverageBase
Indicates that one entity serves as the primary liquid or foundational ingredient used to make a beverage involving the other entity.
-
C.
isSoftDrinkVariantOf
Indicates that one soft drink is a specific version, flavor, or formulation derived from or based on another soft drink.
-
D.
drinks
Indicates that one entity consumes a liquid substance, typically by ingesting it through the mouth.
-
E.
isSoftDrink
Indicates that something is classified as a soft drink, typically a non-alcoholic, carbonated or sweetened beverage.
- 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_69eecda63a3881908095c47900692e65 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 27, 2026, 3:49 a.m.