Triple
T30959974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kare-Kare |
E788783
|
entity |
| Predicate | isTypicallyCookedBy |
P5292
|
FINISHED |
| Object | simmering |
—
|
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: simmering | Statement: [Kare-Kare, isTypicallyCookedBy, simmering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isTypicallyCookedBy Context triple: [Kare-Kare, isTypicallyCookedBy, simmering]
-
A.
isCookedBy
chosen
Indicates that something has been prepared or made ready for eating through cooking by a particular agent.
-
B.
isUsuallyCookedIn
Indicates that something is most commonly or typically prepared or cooked within a particular container, appliance, or environment.
-
C.
worksInKitchenAt
Indicates that an entity performs work or duties in the kitchen area of a specified location or organization.
-
D.
isTypicallyServedFor
Indicates that one item is most commonly or customarily served as a meal or course for the other (e.g., a dish typically served for breakfast, lunch, or dinner).
-
E.
cooksSimilarTo
Indicates that one entity prepares or cooks food in a way that is similar to how another entity does.
- 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_69f224c28c1881908c33b45d689f1724 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69dfdda708190be290c7bec205445 |
completed | May 3, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f69d1a37e081908d1d86b90ff502bd |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 8:54 p.m.