Triple
T12592692
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sangenjaya |
E300644
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Setagaya ward |
E241517
|
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: Setagaya ward | Statement: [Sangenjaya, partOf, Setagaya ward]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Setagaya ward Context triple: [Sangenjaya, partOf, Setagaya ward]
-
A.
Setagaya
chosen
Setagaya is a large residential ward in western Tokyo, Japan, known for its suburban neighborhoods, parks, and role as a commuter area for central Tokyo.
-
B.
Nakano Ward
Nakano Ward is a special ward in western Tokyo, Japan, known for its dense residential neighborhoods, shopping streets, and subculture hubs like Nakano Broadway.
-
C.
Toshima ward
Toshima ward is a special ward in Tokyo, Japan, known for its major commercial and entertainment hub Ikebukuro and its dense urban residential neighborhoods.
-
D.
Bunkyō ward
Bunkyō ward is a central Tokyo district known for its universities, cultural institutions, and quiet residential neighborhoods.
-
E.
Kōtō ward
Kōtō ward is a special ward in eastern Tokyo known for its mix of traditional shitamachi neighborhoods, waterfront areas, and modern residential and commercial districts.
- 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_69d7bde87b648190bcd0266e9efde098 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954cc6d3c81908fbb22601c46f3f7 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006065741c8190ad4ceb6bd3d60f9f |
completed | May 10, 2026, 10:39 a.m. |
Created at: April 9, 2026, 5:07 p.m.