Triple
T35302645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ayam rica-rica |
E1019543
|
entity |
| Predicate | isCommonlyEatenIn |
P56694
|
FINISHED |
| Object | Sulawesi |
—
|
NE NERFINISHED |
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: Sulawesi | Statement: [ayam rica-rica, isCommonlyEatenIn, Sulawesi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isCommonlyEatenIn Context triple: [ayam rica-rica, isCommonlyEatenIn, Sulawesi]
-
A.
isOftenEaten
Indicates that the subject is frequently consumed as food by some agent or group.
-
B.
isEatenIn
chosen
Indicates that one entity (typically food) is consumed within the context, location, or occasion specified by another entity.
-
C.
isTypicallyEatenWith
Indicates that one item is commonly consumed together with another as part of the same eating occasion or dish.
-
D.
isTypicallyConsumedAs
Indicates that one entity is normally or customarily eaten or drunk in the form of another entity.
-
E.
isUsuallyCookedIn
Indicates that something is most commonly or typically prepared or cooked within a particular container, appliance, or environment.
- 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_69f76de8b4c48190ae504b86185c474c |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:03 p.m.