Triple
T10174278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kappabashi-dori |
E235810
|
entity |
| Predicate | connectsArea |
P2564
|
FINISHED |
| Object | Ueno |
E151036
|
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: Ueno | Statement: [Kappabashi-dori, connectsArea, Ueno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ueno Context triple: [Kappabashi-dori, connectsArea, Ueno]
-
A.
Ueno
Ueno is a town in Japan historically known as the birthplace of the renowned haiku poet Matsuo Bashō.
-
B.
Ueno
chosen
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
C.
Komagome
Komagome is a residential and commercial neighborhood in Tokyo known for its traditional atmosphere, historic temples, and the renowned Rikugien Garden.
-
D.
Hibiya
Hibiya is a district in central Tokyo known for its large urban park, theaters, government offices, and proximity to major business and shopping areas.
-
E.
Ueno district
Ueno district is a cultural and historical area in Tokyo known for its major museums, temples, and the expansive Ueno Park.
- 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_69ca84d1d5f88190ab878a1021ecff68 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdeca0dc508190916f2a1bbb288192 |
completed | April 2, 2026, 4:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef8185c6e08190949020a80c24f2b8 |
completed | April 27, 2026, 3:32 p.m. |
Created at: March 30, 2026, 9:11 p.m.