Triple
T3645720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nishi Amane |
E77295
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Nishi Amane |
E77295
|
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: Nishi Amane | Statement: [Nishi Amane, name, Nishi Amane]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nishi Amane Context triple: [Nishi Amane, name, Nishi Amane]
-
A.
Nishi Amane
chosen
Nishi Amane was a pioneering Meiji-era Japanese philosopher and statesman who helped introduce Western philosophy and legal thought to Japan.
-
B.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
C.
Makoto Uchida
Makoto Uchida is a Japanese automotive executive who serves as the chief executive officer of Nissan Motor Co.
-
D.
Nishiarai
Nishiarai is a neighborhood in Tokyo’s Adachi ward known for its historic temples, shopping streets, and residential character.
-
E.
Saitō Makoto
Saitō Makoto was a Japanese admiral and statesman who served as Governor-General of Korea and later as Prime Minister of Japan during the early Shōwa period.
- 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_69ad85de1b988190a45f8dbfebc806fc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc35da84c81908950de92ba171fa3 |
completed | March 8, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5120849188190bea912ed14f90bf3 |
completed | March 14, 2026, 7:45 a.m. |
Created at: March 8, 2026, 3:24 p.m.