Triple
T7426218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Bull Sugarfree |
E171374
|
entity |
| Predicate | hasCalorieContentPer250ml |
P51659
|
FINISHED |
| Object | approximately 10 kcal |
—
|
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 10 kcal | Statement: [Red Bull Sugarfree, hasCalorieContentPer250ml, approximately 10 kcal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCalorieContentPer250ml Context triple: [Red Bull Sugarfree, hasCalorieContentPer250ml, approximately 10 kcal]
-
A.
hasCalories
chosen
Indicates that an entity contains a specified amount of caloric energy.
-
B.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
-
C.
hasSugarFreeVariant
Indicates that an item has a corresponding version or option that is formulated without sugar.
-
D.
lactoseContent
Indicates the amount or presence of lactose contained within a given substance or product.
-
E.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
- 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_69c68a625d048190af70eb8b63bec5a0 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f303eb988190ba9df7946fce1c86 |
completed | March 27, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69c6f03648d08190b862d07fef71210c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:12 p.m.