Triple
T12881691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mango Dragonfruit Refresher |
E308110
|
entity |
| Predicate | caffeineLevel |
P38318
|
FINISHED |
| Object | lightly caffeinated |
—
|
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: lightly caffeinated | Statement: [Mango Dragonfruit Refresher, caffeineLevel, lightly caffeinated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caffeineLevel Context triple: [Mango Dragonfruit Refresher, caffeineLevel, lightly caffeinated]
-
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.
beverageSubcategory
Indicates a more specific classification within a broader beverage category, defining the subtype or subcategory of a drink.
-
E.
coffeeVariety
Indicates a relationship where a specific type or variety of coffee is associated with a coffee-related entity (such as a product, beverage, or plant).
- 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_69d7bdf69bc48190af6c2621f28ca351 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97c7f91d08190aac2f6419d3ba992 |
completed | April 10, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69d96fa55b888190ab1612e93c41aec4 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:39 p.m.