Triple
T22440371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 吹上御苑 |
E554737
|
entity |
| Predicate | 歴史的背景 |
P43371
|
FINISHED |
| Object | 江戸時代には江戸城本丸・西の丸に隣接する庭園・林地として利用されていた |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: 江戸時代には江戸城本丸・西の丸に隣接する庭園・林地として利用されていた | Statement: [吹上御苑, 歴史的背景, 江戸時代には江戸城本丸・西の丸に隣接する庭園・林地として利用されていた]
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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ae1f82881908a611f134eb03f3d |
completed | April 29, 2026, 1:12 a.m. |
Created at: April 16, 2026, 8:47 p.m.