Triple
T20693565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D. T. Suzuki |
E508608
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Kamakura, Japan |
—
|
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: Kamakura, Japan | Statement: [D. T. Suzuki, residence, Kamakura, Japan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamakura, Japan Context triple: [D. T. Suzuki, residence, Kamakura, Japan]
-
A.
Kamakura, Kanagawa, Japan
chosen
Kamakura, Kanagawa, Japan is a historic coastal city south of Tokyo known for its many Buddhist temples, Shinto shrines, and the iconic Great Buddha statue.
-
B.
Kakegawa, Japan
Kakegawa, Japan is a city in Shizuoka Prefecture known for its historic castle, green tea production, and scenic views of Mount Fuji.
-
C.
Kamakura
Kamakura is a historic coastal city in Japan renowned for its Great Buddha statue, numerous temples and shrines, and role as the political center of the Kamakura shogunate.
-
D.
Tamagawa, Japan
Tamagawa, Japan is a small Japanese municipality known for its local community life and cultural exchange ties with international sister cities.
-
E.
Tokuyama, Japan
Tokuyama, Japan is a coastal industrial city in Yamaguchi Prefecture known historically for its port facilities and petrochemical industry.
- 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_69e0b4c1ed408190b72dd26b1e33f8a1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c10fc4088190ab71ef078600954b |
completed | April 21, 2026, 12:13 a.m. |
Created at: April 16, 2026, 12:09 p.m.