Triple
T38231314
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beaufort cheese |
E1012296
|
entity |
| Predicate | typicalFatInDryMatter |
P53132
|
FINISHED |
| Object | about 48% |
—
|
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 48% | Statement: [Beaufort cheese, typicalFatInDryMatter, about 48%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFatInDryMatter Context triple: [Beaufort cheese, typicalFatInDryMatter, about 48%]
-
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.
fattyAcid
Indicates a relationship where one entity is a fatty acid component, derivative, or participant in a process involving another entity.
-
E.
dryMass
Indicates the mass of an object excluding any contained fluids, propellants, or other consumable materials.
- 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_69f76dd25e0c81909f2abd0803e5e3ee |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a1c850c819088795a7ae59bdeb8 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:30 p.m.