Triple
T23136666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomie |
E577341
|
entity |
| Predicate | publisher |
P29
|
FINISHED |
| Object | Kadokawa Shoten |
—
|
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: Kadokawa Shoten | Statement: [Tomie, publisher, Kadokawa Shoten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kadokawa Shoten Context triple: [Tomie, publisher, Kadokawa Shoten]
-
A.
Kadokawa Shoten
chosen
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.
-
B.
Shueisha
Shueisha is a major Japanese publishing company best known for producing popular manga magazines such as Weekly Shōnen Jump.
-
C.
Kodansha
Kodansha is a major Japanese publishing company best known for producing and distributing popular manga, novels, and magazines worldwide.
-
D.
Kadokawa Pictures
Kadokawa Pictures is a Japanese film production and distribution company known for producing genre films, including kaiju movies like "Guardian of the Universe," as well as adaptations of popular novels and manga.
-
E.
Hakusensha
Hakusensha is a Japanese publishing company best known for producing manga magazines and graphic novels.
- 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e8c33308190a44f98a7aab3b670 |
completed | April 29, 2026, 4:52 a.m. |
Created at: April 17, 2026, 4 p.m.