Triple
T1486939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ebisu |
E29485
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Shibuya |
E208724
|
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: Shibuya | Statement: [Ebisu, near, Shibuya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shibuya Context triple: [Ebisu, near, Shibuya]
-
A.
Shibuya
chosen
Shibuya is a major commercial and entertainment district in Tokyo, Japan, famous for its bustling streets, youth culture, and iconic landmarks.
-
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.
Akasaka
Akasaka is a central Tokyo district known for its business centers, upscale hotels, and vibrant nightlife.
-
D.
Harajuku
Harajuku is a vibrant Tokyo district famous for its youth culture, eclectic street fashion, and trendy shopping and entertainment spots.
-
E.
Ikebukuro
Ikebukuro is a major commercial and entertainment district in Tokyo known for its large train station, shopping complexes, and vibrant youth culture.
- 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_69b37371fbc08190abc2d3a1dd2f4e9a |
completed | March 13, 2026, 2:16 a.m. |
Created at: March 1, 2026, 8:12 p.m.