Triple
T16587041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinsei 45 |
E402985
|
entity |
| Predicate | variantOf |
P4680
|
FINISHED |
| Object | Mitsubishi Kinsei |
E1224570
|
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: Mitsubishi Kinsei | Statement: [Kinsei 45, variantOf, Mitsubishi Kinsei]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mitsubishi Kinsei Context triple: [Kinsei 45, variantOf, Mitsubishi Kinsei]
-
A.
Mitsubishi Kinsei
chosen
Mitsubishi Kinsei was a widely used Japanese air-cooled radial aircraft engine of the World War II era, known for powering various Imperial Japanese Navy and Army aircraft.
-
B.
Mitsushō
Mitsushō was a former town in Hokkaido, Japan, that later became part of the newly created town of Shinhidaka through a municipal merger.
-
C.
Mitsubishi Zuisei
The Mitsubishi Zuisei was a Japanese air-cooled radial aircraft engine widely used in Imperial Japanese Navy aircraft during the World War II era.
-
D.
Mitsubishi Jisho Sekkei
Mitsubishi Jisho Sekkei is a major Japanese architectural and urban design firm known for creating prominent high-rise and commercial developments across Japan.
-
E.
Mitsubishi
Mitsubishi is a major Japanese multinational conglomerate known for its diverse businesses in industries such as automotive, heavy industry, finance, and electronics.
- 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_69d88387363c8190a97a0c942130de97 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3599daa508190a9ed6f64138c0e53 |
completed | April 18, 2026, 10:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084b077f481909796b5b60f501239 |
completed | May 10, 2026, 1:14 p.m. |
Created at: April 10, 2026, 5:16 a.m.