Triple
T12881690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mango Dragonfruit Refresher |
E308110
|
entity |
| Predicate | caffeineSource |
P42534
|
FINISHED |
| Object | green coffee extract |
—
|
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: green coffee extract | Statement: [Mango Dragonfruit Refresher, caffeineSource, green coffee extract]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caffeineSource Context triple: [Mango Dragonfruit Refresher, caffeineSource, green coffee extract]
-
A.
typicalCaffeineSource
chosen
Indicates that one entity is a common or characteristic source from which the other entity typically obtains caffeine.
-
B.
carbonSource
Indicates that one entity serves as the source or provider of carbon for another entity or process.
-
C.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
D.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
-
E.
carbonationSource
Indicates the source or method by which something becomes carbonated (i.e., how carbon dioxide is introduced).
- 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.