Triple
T15564689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yorkshire pudding |
E371084
|
entity |
| Predicate | alternativeFat |
P40978
|
FINISHED |
| Object | vegetable oil |
—
|
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: vegetable oil | Statement: [Yorkshire pudding, alternativeFat, vegetable oil]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alternativeFat Context triple: [Yorkshire pudding, alternativeFat, vegetable oil]
-
A.
alternativeForm
Indicates that one entity is an alternative version, variant, or representation of another entity.
-
B.
typicalFatContent
Indicates the usual or characteristic amount of fat contained in something, such as a food or product.
-
C.
alternateDescription
Indicates that an entity has an additional or substitute textual description that can be used in place of its primary description.
-
D.
typicalFatUsed
chosen
Indicates that a particular type of fat is commonly or characteristically used in a given context or application.
-
E.
fatDistribution
Indicates how body fat is spatially allocated or spread across different regions of an entity.
- 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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ddd753c8190b51eaef433258081 |
completed | April 16, 2026, 2:47 a.m. |
| PD | Predicate disambiguation | batch_69deda7e6e748190b29ccce23298afef |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:10 a.m.