Triple
T26879189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Bull Winter Edition |
E676841
|
entity |
| Predicate | hasEnergyDrinkFunction |
P169555
|
FINISHED |
| Object | helps improve concentration |
—
|
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: helps improve concentration | Statement: [Red Bull Winter Edition, hasEnergyDrinkFunction, helps improve concentration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEnergyDrinkFunction Context triple: [Red Bull Winter Edition, hasEnergyDrinkFunction, helps improve concentration]
-
A.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
-
B.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
C.
hasFoodOrDrink
Indicates that an entity possesses, provides, or is associated with some type of food or drink.
-
D.
canBeCarbonated
Indicates that the subject is capable of being made carbonated, typically by dissolving carbon dioxide under pressure.
-
E.
hasCarbonatedWater
Indicates that an entity contains or is associated with carbonated (fizzy) water as a component or ingredient.
- 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_69eee9bb44988190b6e11652d028bc59 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f67fc237608190b6542b56038a7fe4 |
completed | May 2, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
| PDg | Predicate description generation | batch_69f67f0353c88190a05b2db449abe0f4 |
completed | May 2, 2026, 10:47 p.m. |
Created at: April 27, 2026, 5:38 a.m.