Triple
T12572933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 金出 武雄 |
E295648
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Takeo Kanade |
—
|
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: Takeo Kanade | Statement: [金出 武雄, name, Takeo Kanade]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Takeo Kanade Context triple: [金出 武雄, name, Takeo Kanade]
-
A.
Takeo Kanade
chosen
Takeo Kanade is a pioneering Japanese computer scientist and roboticist renowned for his foundational contributions to computer vision, robotics, and autonomous systems.
-
B.
Katsuaki Kamiji
Katsuaki Kamiji is a Japanese politician who serves as the mayor of the city of Yokosuka in Kanagawa Prefecture.
-
C.
Shigetarō Shimada
Shigetarō Shimada was an admiral in the Imperial Japanese Navy who served as Navy Minister and a key naval leader during World War II.
-
D.
Yukio Takasu
Yukio Takasu is a Japanese diplomat who has served in prominent roles at the United Nations, including as Permanent Representative of Japan and UN Under-Secretary-General for Management.
-
E.
Nobuyuki Katsube
Nobuyuki Katsube is a Japanese actor known for his roles in film and television, including a starring performance in the drama "Kumiko, the Treasure Hunter."
- 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_69d6ad9cac2c81908e8a7bed82d1e21d |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d954a52c788190beac128a97e34dc1 |
completed | April 10, 2026, 7:51 p.m. |
Created at: April 8, 2026, 11:50 p.m.