Triple
T8781010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibuya |
E208724
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Sendagaya |
E30009
|
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: Sendagaya | Statement: [Shibuya, contains, Sendagaya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sendagaya Context triple: [Shibuya, contains, Sendagaya]
-
A.
Sendagaya
chosen
Sendagaya is a neighborhood in Tokyo known for its sports facilities, including the National Stadium, and its proximity to Shinjuku and Harajuku.
-
B.
Zoshigaya
Zoshigaya is a quiet, historic residential neighborhood in Tokyo known for its traditional atmosphere, temples, and proximity to Zoshigaya Cemetery.
-
C.
Kumagaya
Kumagaya is a city in northern Saitama Prefecture, Japan, known for its hot summer temperatures and role as a regional commercial and transportation hub.
-
D.
Kanramachi
Kanramachi is a Japanese town known for its cultural and municipal partnership with the Italian town of Certaldo.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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_69ca835fbee88190bf625939bac48d7f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f55b7b08190ab3e18cd634a144b |
completed | March 31, 2026, 11:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6f33ca1081908d65d14c83199c5a |
completed | April 3, 2026, 7:41 a.m. |
Created at: March 30, 2026, 6:42 p.m.