Triple
T10821173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monstress |
E255372
|
entity |
| Predicate | artist |
P184
|
FINISHED |
| Object | Sana Takeda |
E888124
|
NE FINISHED |
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: Sana Takeda | Statement: [Monstress, artist, Sana Takeda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sana Takeda Context triple: [Monstress, artist, Sana Takeda]
-
A.
Sana Takeda
chosen
Sana Takeda is a Japanese illustrator and comic book artist best known for her lush, detailed artwork on the fantasy series *Monstress* and her distinctive blend of manga and Western comic styles.
-
B.
Nadeko Sengoku
Nadeko Sengoku is a shy, soft-spoken middle school girl from the Monogatari Series whose seemingly innocent demeanor hides a darker, aberration-afflicted side central to several story arcs.
-
C.
Yukari Tamura
Yukari Tamura is a popular Japanese voice actress and singer known for her work in anime and J-pop, often performing theme songs for various series.
-
D.
Maki Horikita
Maki Horikita is a Japanese actress known for her leading roles in popular television dramas and films during the 2000s and early 2010s.
-
E.
Yuriko
Yuriko is the given name of Japanese actress Rinko Kikuchi, known for her roles in films such as "Babel" and "Pacific Rim."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73449eee88190afa52c4e6ef96baa |
completed | April 9, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb1096cbc81908f3eda562c2da042 |
completed | April 14, 2026, 9:26 p.m. |
Created at: April 8, 2026, 9:18 p.m.