Triple
T2814304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kantō region |
E54245
|
entity |
| Predicate | containsMajorCity |
P316
|
FINISHED |
| Object | Kawasaki |
E61828
|
NE 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: Kawasaki | Statement: [Kantō region, containsMajorCity, Kawasaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kawasaki Context triple: [Kantō region, containsMajorCity, 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.
Suzuki
Suzuki is a common Japanese surname borne by many notable individuals across sports, entertainment, and other fields.
-
C.
Suzuki Motor Corporation
Suzuki Motor Corporation is a Japanese multinational automaker best known for its compact cars, motorcycles, and all-terrain vehicles sold worldwide.
-
D.
Yamaha Motor Company
Yamaha Motor Company is a Japanese manufacturer best known for its motorcycles, marine products, and power equipment, and is one of the world’s leading powersports brands.
-
E.
Kawasaki Heavy Industries
Kawasaki Heavy Industries is a major Japanese multinational corporation known for manufacturing ships, heavy machinery, aerospace equipment, and transportation systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab49de0af08190b3da69683be1e728 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde4ba34c819085a336498fc326b0 |
completed | March 7, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce9f964081909e422aaf1f026dbb |
completed | March 10, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:59 p.m.