Triple
T16300448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akiko Takeshita |
E395769
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Akiko Takeshita |
—
|
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: Akiko Takeshita | Statement: [Akiko Takeshita, name, Akiko Takeshita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akiko Takeshita Context triple: [Akiko Takeshita, name, Akiko Takeshita]
-
A.
Akiko Takeshita
chosen
Akiko Takeshita is a Japanese actress known internationally for her supporting role in the film "Lost in Translation."
-
B.
Naoko Takeshita
Naoko Takeshita was the wife of former Japanese Prime Minister Noboru Takeshita and a member of a prominent political family in Japan.
-
C.
Takako Hashimoto
Takako Hashimoto is a Japanese former basketball player who competed at the international level, including in the Olympic Games.
-
D.
Akiko Yoshida
Akiko Yoshida is an individual known primarily through her close personal association with Steve Smith.
-
E.
Akiko Yoshida
Akiko Yoshida is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
- 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_69d87f23bb088190a16fbb91a1957ea5 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e25e31c9e8819094593f3aeb44f2ca |
completed | April 17, 2026, 4:22 p.m. |
Created at: April 10, 2026, 5:06 a.m.