Triple
T14167053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 千代田区 |
E351107
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | 秋葉原 |
E71481
|
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: 秋葉原 | Statement: [千代田区, contains, 秋葉原]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 秋葉原 Context triple: [千代田区, contains, 秋葉原]
-
A.
Akihabara
chosen
Akihabara is a famous Tokyo district known as a major center for electronics, anime, manga, and otaku culture.
-
B.
高田馬場
高田馬場 is a bustling neighborhood in Tokyo’s Shinjuku ward known for its major train station, student population, and numerous eateries and entertainment spots.
-
C.
下北沢
下北沢は東京都世田谷区に位置する、古着店やライブハウス、個性的なカフェが集まる若者文化とサブカルチャーの発信地として知られる街です。
-
D.
Yodobashi Hakata
Yodobashi Hakata is a large electronics and retail shopping complex in Fukuoka, Japan, known for its extensive selection of consumer electronics and related goods.
-
E.
Shibuya Mark City
Shibuya Mark City is a large commercial complex in Tokyo’s Shibuya district, featuring offices, a hotel, and a shopping and dining mall directly connected to Shibuya Station.
- 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_69d8278775fc8190b0802d22ca2f495d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61b355f08190864c7322bbcb766d |
completed | April 14, 2026, 3:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7f57ad88190aeb8ee0f834bfa20 |
completed | May 7, 2026, 8:37 p.m. |
Created at: April 10, 2026, 1 a.m.