Triple
T6321374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pepsi Vanilla |
E141745
|
entity |
| Predicate | caffeine |
P38318
|
FINISHED |
| Object | contains caffeine |
—
|
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: contains caffeine | Statement: [Pepsi Vanilla, caffeine, contains caffeine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caffeine Context triple: [Pepsi Vanilla, caffeine, contains caffeine]
-
A.
typicalCaffeineSource
Indicates that one entity is a common or characteristic source from which the other entity typically obtains caffeine.
-
B.
hasCaffeineContent
chosen
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
C.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
-
D.
coffeeBrand
Indicates that one entity is a brand associated with the production or marketing of coffee products for the other entity.
-
E.
nicotineLevel
Indicates the amount or concentration of nicotine present in or associated with an entity.
- 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_69c008d13b8c8190be47d896eb735605 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064c76dfc8190a1d44fd0c4402a0e |
completed | March 22, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69c060e5efc48190861b8266e5b0cc0c |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:29 p.m.