Triple
T22020201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shueisha |
E543823
|
entity |
| Predicate | coOwns |
P3498
|
FINISHED |
| Object | Shogakukan Asia |
—
|
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: Shogakukan Asia | Statement: [Shueisha, coOwns, Shogakukan Asia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shogakukan Asia Context triple: [Shueisha, coOwns, Shogakukan Asia]
-
A.
Shogakukan
chosen
Shogakukan is a major Japanese publishing company best known for producing popular manga, educational materials, and magazines.
-
B.
Hakusensha
Hakusensha is a Japanese publishing company best known for producing manga magazines and graphic novels.
-
C.
Tokyo Shokonsha
Tokyo Shokonsha was the original name of what is now Yasukuni Shrine, a Shinto shrine in Tokyo dedicated to commemorating Japan’s war dead.
-
D.
Kodansha
Kodansha is a major Japanese publishing company best known for producing and distributing popular manga, novels, and magazines worldwide.
-
E.
Tokuma Shoten
Tokuma Shoten is a major Japanese publishing company known for books, magazines, and its historical involvement in anime and media production.
- 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.