Triple
T17436644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Bad Sleep Well |
E424016
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Kyoko Kagawa |
—
|
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: Kyoko Kagawa | Statement: [The Bad Sleep Well, starring, Kyoko Kagawa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kyoko Kagawa Context triple: [The Bad Sleep Well, starring, Kyoko Kagawa]
-
A.
Kyōko Kagawa
chosen
Kyōko Kagawa is a renowned Japanese actress celebrated for her roles in classic films by directors such as Akira Kurosawa and Yasujirō Ozu.
-
B.
Kyoko Takezawa
Kyoko Takezawa is a renowned Japanese violinist celebrated for her virtuosic technique and international concert career.
-
C.
Takako Takahashi
Takako Takahashi is a Japanese writer known for her psychologically complex fiction and contributions to postwar Japanese literature.
-
D.
Yukiko Kobayashi
Yukiko Kobayashi is a Japanese actress best known for her roles in classic Toho kaiju films of the late 1960s and early 1970s.
-
E.
Yuko Shimizu
Yuko Shimizu is a Japanese illustrator and comic artist renowned for her distinctive, dynamic style and contributions to major publications, book covers, 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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4490426008190b474ed76aca5d6f3 |
completed | April 19, 2026, 3:16 a.m. |
Created at: April 10, 2026, 5:46 a.m.