Triple
T28553239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkish coffee |
E722941
|
entity |
| Predicate | typicalSweeteningOptions |
P65313
|
FINISHED |
| Object | unsweetened (sade) |
—
|
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: unsweetened (sade) | Statement: [Turkish coffee, typicalSweeteningOptions, unsweetened (sade)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSweeteningOptions Context triple: [Turkish coffee, typicalSweeteningOptions, unsweetened (sade)]
-
A.
sweetenerType
chosen
Indicates the specific kind or category of sweetener associated with or used in relation to an entity.
-
B.
sweetenerVariant
Indicates that one sweetening agent is a specific type, version, or alternative form of another sweetening agent.
-
C.
containsSweeteners
Indicates that something includes one or more sweetening substances as part of its composition or contents.
-
D.
sweetening
Indicates the action or process of making something taste sweeter, often by adding a sweet substance.
-
E.
sweetenedVersionKnownAs
Indicates that one item is a sweetened form of another and is commonly referred to by a specific name.
- 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_6a00c07b302081908bf9755f8fdbc36e |
completed | May 10, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_6a00bfffb168819084b65804d601896d |
completed | May 10, 2026, 5:27 p.m. |
Created at: April 28, 2026, 3:44 a.m.