Triple
T17573688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anxi County |
E428002
|
entity |
| Predicate | teaBrandRole |
P86675
|
FINISHED |
| Object | origin place for Anxi Tieguanyin |
—
|
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: origin place for Anxi Tieguanyin | Statement: [Anxi County, teaBrandRole, origin place for Anxi Tieguanyin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teaBrandRole Context triple: [Anxi County, teaBrandRole, origin place for Anxi Tieguanyin]
-
A.
teaBrand
chosen
Indicates that one entity is a brand or producer associated with a particular type or product line of tea for the other entity.
-
B.
coffeeBrand
Indicates that one entity is a brand associated with the production or marketing of coffee products for the other entity.
-
C.
teaType
Indicates the specific variety or category of tea associated with an entity.
-
D.
teaCategory
Indicates that one item is classified as belonging to a particular category or type of tea.
-
E.
teaIndustryDevelopedUnder
Indicates that the tea industry grew, expanded, or was significantly shaped under the influence, control, or conditions provided by a particular authority, period, or context.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e459330c788190907a02fc98e0e24b |
completed | April 19, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fd7d048190b54ee4c6155612a5 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.