Triple
T19968109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanto Plain |
E479995
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Kawasaki |
—
|
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: Kawasaki | Statement: [Kanto Plain, containsCity, Kawasaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kawasaki Context triple: [Kanto Plain, containsCity, Kawasaki]
-
A.
Kawasaki
chosen
Kawasaki is a major industrial and residential city in Kanagawa Prefecture, Japan, located between Tokyo and Yokohama along the Tama River.
-
B.
Kawasaki Motors
Kawasaki Motors is the motorcycle, ATV, and small engine manufacturing division of Kawasaki Heavy Industries, known worldwide for its high-performance bikes and power sports vehicles.
-
C.
Kawasaki Daishi
Kawasaki Daishi is a major Shingon Buddhist temple in Kawasaki, Japan, renowned as a popular site for New Year’s visits and prayers for protection from misfortune.
-
D.
Kawasaki More’s
Kawasaki More’s is a shopping complex located in Kawasaki-ku, Japan, offering a variety of retail stores, dining options, and services.
-
E.
Suzuki
Suzuki is a common Japanese surname borne by many notable individuals across sports, entertainment, and other fields.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc6b0208190b1ae30be95712326 |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.