Triple
T22533337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 東京都港区 |
E557098
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | 渋谷区 |
—
|
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: 渋谷区 | Statement: [東京都港区, borders, 渋谷区]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 渋谷区 Context triple: [東京都港区, borders, 渋谷区]
-
A.
Shibuya-ku
chosen
Shibuya-ku is a major commercial and entertainment ward in central Tokyo, Japan, known for its bustling shopping districts, nightlife, and the iconic Shibuya Crossing.
-
B.
東京都港区
東京都港区は、東京湾に面し大使館や企業本社、高級住宅地が集まる東京都心の行政区の一つです。
-
C.
Shinjuku-ku
Shinjuku-ku is a major commercial and administrative ward in central Tokyo, Japan, known for its bustling shopping districts, skyscrapers, and one of the world’s busiest railway stations.
-
D.
Meguro Ward
Meguro Ward is a residential and commercial district in southwest Tokyo known for its urban neighborhoods, cultural sites, and convenient rail access to central Tokyo.
-
E.
Aoyama district
Aoyama district is an upscale neighborhood in central Tokyo known for its fashionable boutiques, trendy cafes, art galleries, and modern architecture.
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed88cf08190ae7e5b6bf9a80372 |
completed | April 29, 2026, 1:28 a.m. |
Created at: April 16, 2026, 8:51 p.m.