Triple
T19418126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cornish clotted cream |
E485773
|
entity |
| Predicate | typicalFatPercentage |
P53132
|
FINISHED |
| Object | about 55–64% |
—
|
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: about 55–64% | Statement: [Cornish clotted cream, typicalFatPercentage, about 55–64%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFatPercentage Context triple: [Cornish clotted cream, typicalFatPercentage, about 55–64%]
-
A.
typicalFatContent
chosen
Indicates the usual or characteristic amount of fat contained in something, such as a food or product.
-
B.
typicalFatUsed
Indicates that a particular type of fat is commonly or characteristically used in a given context or application.
-
C.
fatDistribution
Indicates how body fat is spatially allocated or spread across different regions of an entity.
-
D.
typicalBodyType
Indicates that one entity is the usual or characteristic body type associated with another entity.
-
E.
bodyThickness
Indicates the measured or relative thickness of an entity’s body in the context of a comparison or description.
- 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_69d8e8d688f881909c85104a62e09d8a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62afad8d881908f1de6324e55d2f6 |
completed | April 20, 2026, 1:32 p.m. |
| PD | Predicate disambiguation | batch_69e4fd68b1f881908d273de1fee81a75 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:37 p.m.