Triple
T3955145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huangshan Maofeng tea |
E84957
|
entity |
| Predicate | caffeineContent |
P38318
|
FINISHED |
| Object | moderate |
—
|
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: moderate | Statement: [Huangshan Maofeng tea, caffeineContent, moderate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caffeineContent Context triple: [Huangshan Maofeng tea, caffeineContent, moderate]
-
A.
hasCaffeineContent
chosen
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
-
B.
typicalCaffeineSource
Indicates that one entity is a common or characteristic source from which the other entity typically obtains caffeine.
-
C.
featuresBeverage
Indicates that one entity includes, offers, or presents a particular beverage as part of its contents, services, or characteristics.
-
D.
numberOfCups
Indicates the quantity of cups associated with a given entity or context.
-
E.
isSoftDrinkVariantOf
Indicates that one soft drink is a specific version, flavor, or formulation derived from or based on another soft drink.
- 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_69aed934fbfc8190847068e4546de963 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefaa5afdc8190b709af2473d75d02 |
completed | March 9, 2026, 4:51 p.m. |
| PD | Predicate disambiguation | batch_69aef8ed04e4819096bced8971cd888d |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:30 p.m.