Triple
T17436377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ran |
E424010
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Mieko Harada |
—
|
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: Mieko Harada | Statement: [Ran, starring, Mieko Harada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mieko Harada Context triple: [Ran, starring, Mieko Harada]
-
A.
Mieko Harada
chosen
Mieko Harada is a Japanese actress best known internationally for her intense portrayal of Lady Kaede in Akira Kurosawa’s epic film "Ran."
-
B.
Makiko Tanaka
Makiko Tanaka is a Japanese politician and former foreign minister, known as the outspoken daughter of influential former Prime Minister Kakuei Tanaka.
-
C.
Takako Ohta
Takako Ohta is a Japanese singer and actress best known for her 1980s idol career and hit songs in the J-pop genre.
-
D.
Kumiko Hirano
Kumiko Hirano is a Japanese individual notable enough to be specifically cited as a bearer of the surname Hirano.
-
E.
Takako Konishi
Takako Konishi was a Japanese office worker whose mysterious death in Minnesota in 2001 inspired the urban legend that she had traveled there searching for the fictional buried money from the film "Fargo."
- 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.