Triple
T14437530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taitō ward |
E358002
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Kuramae |
—
|
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: Kuramae | Statement: [Taitō ward, contains, Kuramae]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuramae Context triple: [Taitō ward, contains, Kuramae]
-
A.
Komagome
Komagome is a residential and commercial neighborhood in Tokyo known for its traditional atmosphere, historic temples, and the renowned Rikugien Garden.
-
B.
Kamitabashi
Kamitabashi is a residential neighborhood located in the Kita ward of Tokyo, Japan.
-
C.
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.
-
D.
Kappabashi
chosen
Kappabashi is a famous Tokyo shopping street and district known for its many stores specializing in kitchenware, restaurant supplies, and realistic plastic food models.
-
E.
Kagurazaka
Kagurazaka is a historic neighborhood in central Tokyo known for its narrow cobblestone streets, traditional ryotei restaurants, and blend of old geisha district charm with modern boutiques and cafes.
- 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_69d8279402a88190821ffa39ae15bccf |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de914a45ec81909ab8ccf302047d7f |
completed | April 14, 2026, 7:11 p.m. |
Created at: April 10, 2026, 1:18 a.m.