Triple
T16513702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lu’an Guapian tea |
E401125
|
entity |
| Predicate | teaTypeByOxidation |
P53106
|
FINISHED |
| Object | non-oxidized |
—
|
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: non-oxidized | Statement: [Lu’an Guapian tea, teaTypeByOxidation, non-oxidized]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teaTypeByOxidation Context triple: [Lu’an Guapian tea, teaTypeByOxidation, non-oxidized]
-
A.
teaType
Indicates the specific variety or category of tea associated with an entity.
-
B.
teaCategory
chosen
Indicates that one item is classified as belonging to a particular category or type of tea.
-
C.
teaCulture
Indicates the relationship in which practices, rituals, and social norms surrounding the preparation and consumption of tea are shared, expressed, or maintained.
-
D.
tanninLevel
Indicates the degree or intensity of tannins present in or associated with something, typically a beverage like wine or tea.
-
E.
coffeeDesignationType
Indicates the specific classification or type designation assigned to a coffee (e.g., by quality, origin, or regulatory 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_69d88381f6148190819958a038be990e |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e78d4848190a55de9902115b1b2 |
completed | April 18, 2026, 7:10 a.m. |
| PD | Predicate disambiguation | batch_69e296995d388190b88ebe189dce890d |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:14 a.m.