Triple
T27205375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee |
E683847
|
entity |
| Predicate | averageCaffeineContent |
P38318
|
FINISHED |
| Object | About 95 milligrams per 240 milliliters of brewed coffee |
—
|
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: About 95 milligrams per 240 milliliters of brewed coffee | Statement: [Coffee, averageCaffeineContent, About 95 milligrams per 240 milliliters of brewed coffee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageCaffeineContent Context triple: [Coffee, averageCaffeineContent, About 95 milligrams per 240 milliliters of brewed coffee]
-
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.
hasCaffeineFreeOption
Indicates that something offers an available version or option that does not contain caffeine.
- 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_69eefad339a08190aeacb2a198f1a39b |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f64dbbaefc8190952b8320bf4397d8 |
completed | May 2, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f64cacd2c08190aed8a1761d0da679 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 9:37 a.m.