Triple
T6652545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kona coffee-growing region |
E150855
|
entity |
| Predicate | coffeeIntroducedBy |
P72101
|
FINISHED |
| Object | missionaries and early settlers |
—
|
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: missionaries and early settlers | Statement: [Kona coffee-growing region, coffeeIntroducedBy, missionaries and early settlers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coffeeIntroducedBy Context triple: [Kona coffee-growing region, coffeeIntroducedBy, missionaries and early settlers]
-
A.
teaType
Indicates the specific variety or category of tea associated with an entity.
-
B.
coffeeBrand
Indicates that one entity is a brand associated with the production or marketing of coffee products for the other entity.
-
C.
coffeeSourcingRegion
Indicates the geographic region from which the coffee was originally sourced or obtained.
-
D.
hasDrinkNamedAfter
Indicates that one entity has a beverage that is named after another entity.
-
E.
teaCategory
Indicates that one item is classified as belonging to a particular category or type of tea.
- F. None of above. chosen
Provenance (4 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_69c687f2c9508190a60b9aad31d3f358 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6ad071b0081909b96dd4b93414bd1 |
completed | March 27, 2026, 4:15 p.m. |
| PDg | Predicate description generation | batch_69c6cc988c0081909d22b86ca299331c |
completed | March 27, 2026, 6:29 p.m. |
Created at: March 27, 2026, 2:01 p.m.