Triple
T21872409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shigeru Mizuki |
E540038
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Ten Kai |
—
|
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: Ten Kai | Statement: [Shigeru Mizuki, notableWork, Ten Kai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ten Kai Context triple: [Shigeru Mizuki, notableWork, Ten Kai]
-
A.
Ten Kai
chosen
Ten Kai is a notable work by the Japanese manga artist Shigeru Mizuki, known for its connection to his iconic character Kitaro.
-
B.
Tenko
Tenko is a British television drama series set in a World War II Japanese internment camp for women, known for its intense character-driven storytelling and ensemble cast.
-
C.
Taikse
Taikse is a small village located in Järva County in central Estonia.
-
D.
Kaiyukan
Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
-
E.
Ten Rujun
Ten Rujun is a Chinese actor best known for his role in the acclaimed film "Red Sorghum," which helped bring international attention to Chinese cinema.
- 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f3368d488190a37224b587858ab0 |
completed | April 28, 2026, 5:49 p.m. |
Created at: April 16, 2026, 6:59 p.m.