Triple
T7426263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Bull Editions |
E171375
|
entity |
| Predicate | containsTaurine |
P76912
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Red Bull Editions, containsTaurine, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsTaurine Context triple: [Red Bull Editions, containsTaurine, true]
-
A.
containsAdditives
Indicates that one entity includes or is composed of additional substances or ingredients beyond its primary or original components.
-
B.
traditionallyBelievedToContain
Indicates that something is customarily or historically thought to include or hold something else, regardless of whether this belief is factually accurate.
-
C.
containsAllergenicCompound
Indicates that the subject entity includes one or more compounds known to cause allergic reactions.
-
D.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
E.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
- F. None of above. chosen
Provenance (4 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_69c68a63491881909281f73d4d5643bf |
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. |
| PDg | Predicate description generation | batch_69c6f1ee5ab8819091082324f2dc3b8c |
completed | March 27, 2026, 9:09 p.m. |
Created at: March 27, 2026, 3:12 p.m.