Triple
T16635114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Camellia sinensis var. sinensis |
E404177
|
entity |
| Predicate | teaTypeAssociation |
P53105
|
FINISHED |
| Object | high-quality green teas |
—
|
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-quality green teas | Statement: [Camellia sinensis var. sinensis, teaTypeAssociation, high-quality green teas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teaTypeAssociation Context triple: [Camellia sinensis var. sinensis, teaTypeAssociation, high-quality green teas]
-
A.
teaType
chosen
Indicates the specific variety or category of tea associated with an entity.
-
B.
teaCategory
Indicates that one item is classified as belonging to a particular category or type of tea.
-
C.
hasTeeType
Indicates that an entity (typically a golf hole or course) is associated with a specific type or category of tee.
-
D.
teaBrand
Indicates that one entity is a brand or producer associated with a particular type or product line of tea for the other entity.
-
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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378e999d48190bff680040dbc883d |
completed | April 18, 2026, 12:28 p.m. |
| PD | Predicate disambiguation | batch_69e296ad3f148190af09223dc35b155c |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:17 a.m.