Triple
T12845583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arakawa |
E307166
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Bunkyō |
E469604
|
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: Bunkyō | Statement: [Arakawa, borders, Bunkyō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bunkyō Context triple: [Arakawa, borders, Bunkyō]
-
A.
Bunkyō
chosen
Bunkyō is a central Tokyo ward known for its universities, historic temples, and quiet residential neighborhoods.
-
B.
Toshima
Toshima is a special ward in northwest Tokyo known for the major commercial and entertainment hub of Ikebukuro and its dense urban residential districts.
-
C.
Toshima
Toshima is a small, sparsely populated volcanic island and village in Tokyo’s Izu Islands, known for its natural scenery and traditional rural lifestyle.
-
D.
Kōtō
Kōtō is a special ward in eastern Tokyo, Japan, known for its mix of residential neighborhoods, waterfront areas, and commercial districts.
-
E.
Hamamatsuchō
Hamamatsuchō is a business and transportation district in Tokyo known for its major train and monorail stations, office towers, and proximity to Tokyo Bay.
- 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_69d7bdf5e7cc8190be357278bc5ba3bb |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96ff49efc8190bd6bbac510cc4705 |
completed | April 10, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170d8c9f0819099a398814f49f0ed |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 9, 2026, 5:36 p.m.