Triple
T11603960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibuya-ku |
E275204
|
entity |
| Predicate | contains |
P35
|
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: [Shibuya-ku, contains, Daikanyama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daikanyama Context triple: [Shibuya-ku, contains, Daikanyama]
-
A.
Daikanyama
chosen
Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
-
B.
Fujinomiya
Fujinomiya is a city in Shizuoka Prefecture, Japan, known as a major gateway to Mount Fuji and for its scenic views of the iconic volcano.
-
C.
Okachimachi
Okachimachi is a bustling commercial and shopping district in Tokyo known for its discount stores, jewelry shops, and proximity to Ueno.
-
D.
Kyotanabe
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d895502e0081909ee9c3d45d26cd91 |
completed | April 10, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f79c6ac81909935dace3dcc8bea |
completed | May 10, 2026, 6:02 a.m. |
Created at: April 8, 2026, 9:38 p.m.