Triple
T2538740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiener Schnitzel |
E56331
|
entity |
| Predicate | meatPreparation |
P40977
|
FINISHED |
| Object | thinly pounded |
—
|
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: thinly pounded | Statement: [Wiener Schnitzel, meatPreparation, thinly pounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: meatPreparation Context triple: [Wiener Schnitzel, meatPreparation, thinly pounded]
-
A.
typicalMeat
Indicates that something is commonly or characteristically used or regarded as meat in a given context.
-
B.
notableMeatProduct
Indicates that one entity is a meat-based product that is especially prominent, well-known, or significant in relation to the other entity.
-
C.
commonMeatCut
Indicates that two items are the same or equivalent cut of meat, or that an item belongs to a standard, commonly recognized meat cut category.
-
D.
meatQuality
Indicates the assessed level or characteristics of quality associated with a given piece or type of meat.
-
E.
traditionalPreparation
Indicates that something is prepared or made using customary, long-established methods or techniques associated with a particular culture or practice.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69abd648487881908ce8ca22def77294 |
completed | March 7, 2026, 7:39 a.m. |
Created at: March 6, 2026, 9:47 p.m.