Triple
T15466366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Water Park |
E372039
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Shiho Fujii |
E948713
|
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: Shiho Fujii | Statement: [Water Park, musicBy, Shiho Fujii]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shiho Fujii Context triple: [Water Park, musicBy, Shiho Fujii]
-
A.
Shiho Fujii
chosen
Shiho Fujii is a Japanese video game composer best known for her work on Nintendo titles, including contributing music to Super Mario Odyssey.
-
B.
Miyako Fujitani
Miyako Fujitani is a Japanese aikido instructor and former actress best known as Steven Seagal’s first wife and a key figure in his early martial arts career in Japan.
-
C.
Yuki Shimoda
Yuki Shimoda was a Japanese-American actor known for his work on stage and screen, including notable roles in Broadway productions and Hollywood films.
-
D.
Mie Fukuda
Mie Fukuda is best known as the wife of former Japanese Prime Minister Takeo Fukuda.
-
E.
Mieko Harada
Mieko Harada is a Japanese actress best known internationally for her intense portrayal of Lady Kaede in Akira Kurosawa’s epic film "Ran."
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f680cec8190836a5ec841dee224 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6ec4e868819092739e71118d43b0 |
completed | May 9, 2026, 5:28 p.m. |
Created at: April 10, 2026, 3:33 a.m.