Triple
T30302498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tea |
E770687
|
entity |
| Predicate | hasCaffeineLevel |
P38318
|
FINISHED |
| Object | High in black tea |
—
|
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: High in black tea | Statement: [Tea, hasCaffeineLevel, High in black tea]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaffeineLevel Context triple: [Tea, hasCaffeineLevel, High in black tea]
-
A.
hasCaffeineContent
chosen
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
B.
hasCaffeinatedOption
Indicates that something offers or includes at least one option that contains caffeine.
-
C.
typicalCaffeineSource
Indicates that one entity is a common or characteristic source from which the other entity typically obtains caffeine.
-
D.
hasCaffeineFreeOption
Indicates that something offers an available version or option that does not contain caffeine.
-
E.
hasBeverageCategory
Indicates that an entity is associated with or classified under a particular beverage category.
- 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_69f224881b948190b8c4921b250a44a3 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fdd92396788190ae1424bc1ae55844 |
completed | May 8, 2026, 12:37 p.m. |
| PD | Predicate disambiguation | batch_69fdd678f40481909a717a2daec83b36 |
completed | May 8, 2026, 12:26 p.m. |
Created at: April 29, 2026, 7:49 p.m.