Triple
T36286210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raisin Bran |
E893090
|
entity |
| Predicate | canBeConsumedWith |
P198427
|
FINISHED |
| Object | yogurt |
—
|
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: yogurt | Statement: [Raisin Bran, canBeConsumedWith, yogurt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeConsumedWith Context triple: [Raisin Bran, canBeConsumedWith, yogurt]
-
A.
isTypicallyConsumedFrom
Indicates that one entity is most commonly eaten or drunk using, contained in, or taken from the other entity.
-
B.
canConsume
Indicates that one entity is able or permitted to consume, ingest, or use another entity.
-
C.
isTypicallyConsumedAfter
Indicates that one item is most commonly eaten, drunk, or otherwise consumed after another item in time or sequence.
-
D.
isTypicallyConsumedAs
Indicates that one entity is normally or customarily eaten or drunk in the form of another entity.
-
E.
isConsumedIn
Indicates that one entity is used up, ingested, or otherwise expended as part of a process, event, or action involving another entity.
- 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_69f76e4955c08190b8cfddca34fc0242 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fee25dbca481909e6f1c255122b3a8 |
completed | May 9, 2026, 7:29 a.m. |
| PD | Predicate disambiguation | batch_69fee1c8915c8190b08b63e42881f1a9 |
completed | May 9, 2026, 7:27 a.m. |
| PDg | Predicate description generation | batch_69fee25c7c548190a2c6e50074a33da4 |
completed | May 9, 2026, 7:29 a.m. |
Created at: May 3, 2026, 4:09 p.m.