Triple
T4193356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr Pepper |
E89085
|
entity |
| Predicate | hasCaffeinatedOption |
P54585
|
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: [Dr Pepper, hasCaffeinatedOption, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaffeinatedOption Context triple: [Dr Pepper, hasCaffeinatedOption, true]
-
A.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
B.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
C.
hasBeverageProgram
Indicates that an entity offers, manages, or participates in an organized beverage-related offering or initiative.
-
D.
typicalCaffeineSource
Indicates that one entity is a common or characteristic source from which the other entity typically obtains caffeine.
-
E.
hasTeeType
Indicates that an entity (typically a golf hole or course) is associated with a specific type or category of tee.
- 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_69aed9569a4481908b6c1fcec2a11e21 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af04b009dc8190abda3f149a5b16fa |
completed | March 9, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69af01935064819096b7619f42e164dd |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af04af4e44819098a9d7f91e65adf2 |
completed | March 9, 2026, 5:34 p.m. |
Created at: March 9, 2026, 3:46 p.m.