Triple
T2538763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wiener Schnitzel |
E56331
|
entity |
| Predicate | traditionalUtensilForPounding |
P24520
|
FINISHED |
| Object | meat mallet |
—
|
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: meat mallet | Statement: [Wiener Schnitzel, traditionalUtensilForPounding, meat mallet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalUtensilForPounding Context triple: [Wiener Schnitzel, traditionalUtensilForPounding, meat mallet]
-
A.
eatenWithUtensil
Indicates that something was eaten using a specific utensil as the means of consumption.
-
B.
traditionallyUsedBy
Indicates that something has been customarily or historically used by a particular person, group, or culture over time.
-
C.
isToolOf
chosen
Indicates that one entity functions as an instrument or means used by another entity to perform tasks or achieve goals.
-
D.
traditionalPreparation
Indicates that something is prepared or made using customary, long-established methods or techniques associated with a particular culture or practice.
-
E.
dishType
Indicates the classification of a dish according to its culinary category or role (e.g., appetizer, main course, dessert).
- 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.