Triple
T2338142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Mountains coffee |
E44358
|
entity |
| Predicate | hasCaffeineContent |
P38318
|
FINISHED |
| Object | similar to other Arabica coffees |
—
|
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: similar to other Arabica coffees | Statement: [Blue Mountains coffee, hasCaffeineContent, similar to other Arabica coffees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaffeineContent Context triple: [Blue Mountains coffee, hasCaffeineContent, similar to other Arabica coffees]
-
A.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
-
B.
featuresBeverage
Indicates that one entity includes, offers, or presents a particular beverage as part of its contents, services, or characteristics.
-
C.
hasTeeType
Indicates that an entity (typically a golf hole or course) is associated with a specific type or category of tee.
-
D.
hasSugarContent
Indicates that one entity possesses or contains a specified amount or level of sugar.
-
E.
traditionalDrink
Indicates that one entity is a beverage customarily consumed within the culture, heritage, or longstanding practices associated with another entity.
- 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_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. |
| PDg | Predicate description generation | batch_69abc6f4245881909282b3184a288e2a |
completed | March 7, 2026, 6:34 a.m. |
Created at: March 4, 2026, 7:51 p.m.