Triple
T2338109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Mountains coffee |
E44358
|
entity |
| Predicate | isConsideredSpecialtyCoffee |
P32264
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Blue Mountains coffee, isConsideredSpecialtyCoffee, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isConsideredSpecialtyCoffee Context triple: [Blue Mountains coffee, isConsideredSpecialtyCoffee, true]
-
A.
hasSpecialtyFood
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
B.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
C.
coffeeSourcingRegion
Indicates the geographic region from which the coffee was originally sourced or obtained.
-
D.
hasTeeType
Indicates that an entity (typically a golf hole or course) is associated with a specific type or category of tee.
-
E.
hasSpecialCategory
chosen
Indicates that an entity is associated with a designated special or exceptional category distinct from its standard classifications.
- 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_69a889132b488190bbb43ad4780ddd92 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6f75d888190a2e41edaa532e83f |
completed | March 7, 2026, 6:34 a.m. |
| PD | Predicate disambiguation | batch_69abc594087c819098100a10c5478a4b |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:51 p.m.