Triple
T21007509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pikmin 3 |
E517450
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | Shigefumi Hino |
—
|
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: Shigefumi Hino | Statement: [Pikmin 3, director, Shigefumi Hino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shigefumi Hino Context triple: [Pikmin 3, director, Shigefumi Hino]
-
A.
Shigefumi Hino
chosen
Shigefumi Hino is a Japanese video game artist and designer best known for his character and world design work on Nintendo’s Super Mario and Yoshi series.
-
B.
Koichi Satō
Koichi Satō is a prominent Japanese actor known for his versatile performances in film, television, and theater.
-
C.
Motohiro Ōno
Motohiro Ōno is a Japanese politician who serves as the governor of Saitama Prefecture.
-
D.
Haruo Nakajima
Haruo Nakajima was a Japanese actor and stunt performer best known for originating the role of Godzilla and portraying numerous kaiju in Toho’s classic monster films.
-
E.
Yoshio Tsuchiya
Yoshio Tsuchiya was a Japanese actor best known for his roles in classic science fiction and kaiju films, particularly within the Godzilla franchise.
- 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_69e0b50192308190a284fcc89dd23a49 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc3cc8648190b1a419ef734a69e6 |
completed | April 21, 2026, 4:25 a.m. |
Created at: April 16, 2026, 1:53 p.m.