Triple
T21090380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mie Hama |
E519621
|
entity |
| Predicate | portrayedCharacter |
P1668
|
FINISHED |
| Object | Kissy Suzuki |
—
|
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: Kissy Suzuki | Statement: [Mie Hama, portrayedCharacter, Kissy Suzuki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kissy Suzuki Context triple: [Mie Hama, portrayedCharacter, Kissy Suzuki]
-
A.
Kissy Suzuki
chosen
Kissy Suzuki is a Japanese secret agent and Bond girl who assists James Bond in his mission in the 1967 film adaptation of "You Only Live Twice."
-
B.
Kaori Momoi
Kaori Momoi is a renowned Japanese actress and director known for her versatile performances in both domestic cinema and international films.
-
C.
Chika Fujiwara
Chika Fujiwara is a bubbly, unpredictable, and musically talented student council member known for her comedic antics in the manga and anime series "Kaguya-sama: Love Is War."
-
D.
Seiko Matsuda
Seiko Matsuda is a hugely popular Japanese pop singer and idol, especially famous in the 1980s, known for her numerous hit songs and enduring influence on J-pop culture.
-
E.
Rina Satō
Rina Satō is a Japanese voice actress known for her roles in numerous anime series, video games, and other media.
- 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_69e0b507dd9081908fb8bfcbef4c8b46 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7094ea7f881909db83bf6961b41ec |
completed | April 21, 2026, 5:21 a.m. |
Created at: April 16, 2026, 2:50 p.m.