Triple
T15433336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nogizaka |
E369694
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Akasaka |
E235541
|
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: Akasaka | Statement: [Nogizaka, near, Akasaka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Akasaka Context triple: [Nogizaka, near, Akasaka]
-
A.
Akasaka
chosen
Akasaka is a central Tokyo district known for its business centers, upscale hotels, and vibrant nightlife.
-
B.
Shinjuku
Shinjuku is a major commercial and entertainment district in western Tokyo, known for its busy railway station, skyscrapers, shopping, nightlife, and the Tokyo Metropolitan Government Building.
-
C.
Roppongi
Roppongi is a central Tokyo district famous for its vibrant nightlife, international community, and major art and entertainment complexes.
-
D.
Shibuya
Shibuya is a major commercial and entertainment district in Tokyo, Japan, famous for its bustling streets, youth culture, and iconic landmarks.
-
E.
Minami-Aoyama
Minami-Aoyama is an upscale district in Tokyo’s Minato ward known for its fashionable boutiques, stylish cafes, and contemporary art galleries.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03eda01cc8190843e23b260b8503c |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01953c493c819084850ab8e7f0d261 |
completed | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 3:21 a.m.