Triple
T28553211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish coffee |
E722941
|
entity |
| Predicate | grindSize |
P120526
|
FINISHED |
| Object | very fine |
—
|
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: very fine | Statement: [Turkish coffee, grindSize, very fine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grindSize Context triple: [Turkish coffee, grindSize, very fine]
-
A.
grindType
chosen
Indicates the specific way or degree to which something is ground or milled, such as the fineness or style of grinding applied.
-
B.
grainSize
Indicates the relative coarseness or fineness of the material or particles involved in the relationship.
-
C.
grain
Indicates that one entity is composed of or contains a granular substance or small particles of another entity.
-
D.
estimatedTeaWeight
Indicates the quantified amount of tea that is approximated or predicted in weight rather than precisely measured.
-
E.
grainPolishing
Indicates the process of smoothing or refining the surface of a grain or granular material, typically by abrasion or buffing, to improve its texture, appearance, or performance.
- 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_69f01a60204481909af1bb76247b8221 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f6504d2594819085cc5d1276b388ac |
completed | May 2, 2026, 7:28 p.m. |
| PD | Predicate disambiguation | batch_69f64cb0d8008190912e1430cfaf92aa |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 28, 2026, 3:44 a.m.