Triple
T1173834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korean cuisine |
E24971
|
entity |
| Predicate | typicalMealStructure |
P26228
|
FINISHED |
| Object | bap with banchan |
—
|
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: bap with banchan | Statement: [Korean cuisine, typicalMealStructure, bap with banchan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalMealStructure Context triple: [Korean cuisine, typicalMealStructure, bap with banchan]
-
A.
feedingStructure
Indicates a relationship where one entity serves as the anatomical or mechanical structure used by another entity to obtain or ingest food.
-
B.
typicalSchedule
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
-
C.
hasMealType
Indicates that an entity is associated with a specific category or type of meal (such as breakfast, lunch, or dinner).
-
D.
feastType
Indicates the specific kind or category of feast associated with an event or occasion.
-
E.
typicalMeat
Indicates that something is commonly or characteristically used or regarded as meat in a given context.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd53e4b48190abb2167f8074a6bc |
completed | March 1, 2026, 10:27 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5844348190b01ac6506906ba3b |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bd52177081908c5cec8e731b836e |
completed | March 1, 2026, 10:27 p.m. |
Created at: March 1, 2026, 7:45 p.m.