Triple
T316201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khinkali |
E7711
|
entity |
| Predicate | typicalFilling |
P5084
|
FINISHED |
| Object | mixed beef and pork |
—
|
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: mixed beef and pork | Statement: [Khinkali, typicalFilling, mixed beef and pork]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFilling Context triple: [Khinkali, typicalFilling, mixed beef and pork]
-
A.
typicalFlavor
Indicates that something characteristically has or is associated with a particular flavor.
-
B.
typicalFeatures
chosen
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
C.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
-
D.
typicalProductionType
Indicates the usual or characteristic type of production activity associated with an entity.
-
E.
typicalBackground
Indicates that an entity has a usual or commonly expected background, context, or setting associated with it.
- 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_69a2e7e7af7881908890039d6be4e9b8 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ea6462148190825acc57f6d2adaf |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e943f12c8190883854aeed974260 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.