Triple
T11522530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hideo Saito |
E273201
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Hideo Saito |
—
|
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: Hideo Saito | Statement: [Hideo Saito, name, Hideo Saito]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hideo Saito Context triple: [Hideo Saito, name, Hideo Saito]
-
A.
Hideo Saito
chosen
Hideo Saito was a prominent Japanese cellist and conductor renowned as a pioneering music educator who trained many leading conductors, including Seiji Ozawa.
-
B.
Koichi Hayashida
Koichi Hayashida is a Japanese video game designer best known for his prominent work on Nintendo’s Super Mario series, particularly the 3D platformer entries.
-
C.
Koichi Tanaka
Koichi Tanaka is a Japanese engineer and Nobel Prize–winning chemist renowned for his pioneering work in mass spectrometry, particularly soft laser desorption ionization.
-
D.
Takeshi Takashina
Takeshi Takashina was an Imperial Japanese Army general best known for leading Japanese forces during World War II, including in the Pacific campaigns.
-
E.
Makoto Yamashita
Makoto Yamashita is a Japanese politician serving as the governor of Nara Prefecture.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d87fd129c88190893b95222480e04a |
completed | April 10, 2026, 4:42 a.m. |
Created at: April 8, 2026, 9:37 p.m.