Triple
T17533209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Koizumi Kyoko |
E426989
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Koizumi |
—
|
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: Koizumi | Statement: [Koizumi Kyoko, familyName, Koizumi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koizumi Context triple: [Koizumi Kyoko, familyName, Koizumi]
-
A.
Koizumi
chosen
Koizumi is a Japanese surname most prominently associated with Junichiro Koizumi, a former Prime Minister of Japan known for his reformist policies.
-
B.
Nezu
Nezu is a traditional neighborhood in Tokyo known for its historic Nezu Shrine, old-town atmosphere, and preserved shitamachi streets.
-
C.
Kashiba
Kashiba is a city in Japan known for its residential communities and location in the northwestern part of Nara Prefecture, near the Osaka metropolitan area.
-
D.
Koizumi Yakumo
Koizumi Yakumo, born Lafcadio Hearn, was a Greek-Irish writer who became a naturalized Japanese citizen and is renowned for his collections of Japanese ghost stories and essays on Japanese culture.
-
E.
Tomiichi
Tomiichi is a Japanese politician best known for serving as Prime Minister of Japan in the mid-1990s.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4536a0f588190ade91d32308897a0 |
completed | April 19, 2026, 4 a.m. |
Created at: April 10, 2026, 5:49 a.m.