Triple
T17102430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Komae |
E415011
|
entity |
| Predicate | neighboringMunicipality |
P17964
|
FINISHED |
| Object | Setagaya |
—
|
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: Setagaya | Statement: [Komae, neighboringMunicipality, Setagaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Setagaya Context triple: [Komae, neighboringMunicipality, Setagaya]
-
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.
Nishi-Ogikubo
Nishi-Ogikubo is a Tokyo neighborhood known for its laid-back residential atmosphere, vintage and antique shops, and small independent cafes and bars.
-
C.
Itabashi
Itabashi is a special ward in northern Tokyo, Japan, known as a primarily residential area with a mix of traditional neighborhoods and modern urban infrastructure.
-
D.
Shinagawa
Shinagawa is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station, business districts, and waterfront developments.
-
E.
Nakameguro
Nakameguro is a trendy Tokyo neighborhood known for its cherry tree–lined Meguro River, stylish cafes, boutiques, and vibrant nightlife.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2495c88190b5b16a006a994faf |
completed | April 18, 2026, 7:31 p.m. |
Created at: April 10, 2026, 5:35 a.m.