Triple
T1486940
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ebisu |
E29485
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Daikanyama |
E29830
|
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: Daikanyama | Statement: [Ebisu, near, Daikanyama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daikanyama Context triple: [Ebisu, near, Daikanyama]
-
A.
Daikanyama
chosen
Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
-
B.
Kyotanabe
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
-
C.
Tanabe
Tanabe is a coastal city in Japan known as a gateway to the Kumano Kodo pilgrimage routes and for its scenic natural landscapes.
-
D.
Yawata
Yawata is a city in Japan known for its historic Iwashimizu Hachimangū Shrine and its location in the southern part of Kyoto Prefecture.
-
E.
Tomakomai
Tomakomai is an industrial port city on the southern coast of Hokkaido, Japan, known for its paper manufacturing, shipping, and ferry connections.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6a3325881909bbc55efc04ad60f |
completed | March 1, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b26188fe788190a4e7055b5a507006 |
completed | March 12, 2026, 6:47 a.m. |
Created at: March 1, 2026, 8:12 p.m.