Triple
T1104552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | bibimbap |
E25456
|
entity |
| Predicate | typicallyServedAtTemperature |
P19296
|
FINISHED |
| Object | warm |
—
|
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: warm | Statement: [bibimbap, typicallyServedAtTemperature, warm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicallyServedAtTemperature Context triple: [bibimbap, typicallyServedAtTemperature, warm]
-
A.
requiresCookingTemperature
Indicates that performing the action or preparing the item necessitates reaching or maintaining a specific cooking temperature.
-
B.
servingStyle
Indicates how something (typically food or drink) is presented or offered for consumption or use.
-
C.
hasTemperature
Indicates that an entity possesses or is characterized by a specific temperature value.
-
D.
typicalTemperature
chosen
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
E.
servesAgeRange
Indicates that a service, product, or offering is intended for or applicable to entities within a specified age range.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9e1047481909af1cf8df2a01fff |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b7472c848190b0643872f67084a2 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.