Triple
T22020233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Super Dash Bunko |
E543824
|
entity |
| Predicate | hasPublisher |
P1760
|
FINISHED |
| Object | Shueisha |
—
|
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: Shueisha | Statement: [Super Dash Bunko, hasPublisher, Shueisha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shueisha Context triple: [Super Dash Bunko, hasPublisher, Shueisha]
-
A.
Shueisha
chosen
Shueisha is a major Japanese publishing company best known for producing popular manga magazines such as Weekly Shōnen Jump.
-
B.
Kadokawa Shoten
Kadokawa Shoten is a major Japanese publishing company known for producing manga, light novels, and magazines, and for its significant influence on otaku and pop culture media.
-
C.
Kodansha
Kodansha is a major Japanese publishing company best known for producing and distributing popular manga, novels, and magazines worldwide.
-
D.
Hakusensha
Hakusensha is a Japanese publishing company best known for producing manga magazines and graphic novels.
-
E.
Tokyopop
Tokyopop is a North American publisher best known for popularizing Japanese manga in the West and releasing English-language editions of numerous anime and pop-culture tie-in titles.
- 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_69e11e2e8ea4819084210fe06d3a1b8d |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f127c6b5fc8190bc49ddb058f28a44 |
completed | April 28, 2026, 9:33 p.m. |
Created at: April 16, 2026, 8:23 p.m.