Triple
T17911020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sailor Moon |
E447818
|
entity |
| Predicate | publisher |
P29
|
FINISHED |
| Object | Kodansha |
—
|
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: Kodansha | Statement: [Sailor Moon, publisher, Kodansha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kodansha Context triple: [Sailor Moon, publisher, Kodansha]
-
A.
Kodansha
chosen
Kodansha is a major Japanese publishing company best known for producing and distributing popular manga, novels, and magazines worldwide.
-
B.
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.
-
C.
Shueisha
Shueisha is a major Japanese publishing company best known for producing popular manga magazines such as Weekly Shōnen Jump.
-
D.
Chūō Kōron Shinsha
Chūō Kōron Shinsha is a major Japanese publishing company known for its influential literary and cultural magazines and books.
-
E.
Bunko-dō publishing house
Bunko-dō publishing house was a Japanese publisher known for its association with prominent literary figures such as poet Yosano Akiko.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49ea017d081908be850a39edf601f |
completed | April 19, 2026, 9:21 a.m. |
Created at: April 10, 2026, 10:19 a.m.