Triple
T19473461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fujinomiya yakisoba |
E487182
|
entity |
| Predicate | oftenToppedWith |
P5293
|
FINISHED |
| Object | aonori |
—
|
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: aonori | Statement: [Fujinomiya yakisoba, oftenToppedWith, aonori]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: oftenToppedWith Context triple: [Fujinomiya yakisoba, oftenToppedWith, aonori]
-
A.
isTypicallyGarnishedWith
Indicates that one item is commonly used as a garnish or decorative finishing element for another.
-
B.
isToppedWith
chosen
Indicates that one entity serves as a topping placed on the surface of another entity.
-
C.
servesMostly
Indicates that one entity primarily functions to serve, support, or cater to another entity, more than to any other.
-
D.
treatsOf
Indicates that a work, statement, or discourse deals with, discusses, or is concerned with a particular subject or topic.
-
E.
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).
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633ea604c8190a2feacb709b7ca27 |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.