Triple
T2538755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiener Schnitzel |
E56331
|
entity |
| Predicate | commonMislabeling |
P2289
|
FINISHED |
| Object | pork schnitzel sold as Wiener Schnitzel outside Austria |
—
|
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: pork schnitzel sold as Wiener Schnitzel outside Austria | Statement: [Wiener Schnitzel, commonMislabeling, pork schnitzel sold as Wiener Schnitzel outside Austria]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonMislabeling Context triple: [Wiener Schnitzel, commonMislabeling, pork schnitzel sold as Wiener Schnitzel outside Austria]
-
A.
usedLabel
Indicates that one entity has applied, assigned, or referenced a particular label to another entity or resource.
-
B.
misinterpretedBy
Indicates that something (such as a statement, action, or signal) is understood incorrectly or in a way not intended by a particular entity.
-
C.
oftenConfusedWith
chosen
Indicates that one entity is frequently mistaken for or thought to be another due to similarity or ambiguity.
-
D.
ownedLabel
Indicates that a label is possessed or controlled by a particular owner or entity.
-
E.
notableMisconception
Indicates that a commonly held but incorrect belief or understanding exists about the subject.
- 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.