Triple
T2538762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiener Schnitzel |
E56331
|
entity |
| Predicate | typicalThicknessBeforeCooking |
P35314
|
FINISHED |
| Object | pounded to a few millimeters |
—
|
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: pounded to a few millimeters | Statement: [Wiener Schnitzel, typicalThicknessBeforeCooking, pounded to a few millimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalThicknessBeforeCooking Context triple: [Wiener Schnitzel, typicalThicknessBeforeCooking, pounded to a few millimeters]
-
A.
thickness
Indicates the measure of how deep or wide an object or layer is from one surface or side to its opposite.
-
B.
hasCrustType
Indicates that an entity (such as a pizza or pie) is associated with a specific type or style of crust.
-
C.
typicalPreparation
chosen
Indicates the usual or standard way in which something is prepared or made.
-
D.
isCookedBy
Indicates that something has been prepared or made ready for eating through cooking by a particular agent.
-
E.
typicalSauceConsistency
Indicates that something has the usual or characteristic thickness or texture expected of a sauce.
- 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_69ab4a49b6508190bc467fbef4bac334 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd64a2194819097c66cbeb37fe859 |
completed | March 7, 2026, 7:39 a.m. |
| PD | Predicate disambiguation | batch_69abd0c4a5dc819097812db50443420a |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:47 p.m.