Triple
T1486941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ebisu |
E29485
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Meguro |
E251913
|
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: Meguro | Statement: [Ebisu, near, Meguro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meguro Context triple: [Ebisu, near, Meguro]
-
A.
Shinagawa
Shinagawa is a major commercial and transportation hub in Tokyo, Japan, known for its busy railway station, business districts, and waterfront developments.
-
B.
Meguro Ward
chosen
Meguro Ward is a residential and commercial district in southwest Tokyo known for its urban neighborhoods, cultural sites, and convenient rail access to central Tokyo.
-
C.
Setagaya
Setagaya is a large residential ward in western Tokyo, Japan, known for its suburban neighborhoods, parks, and role as a commuter area for central Tokyo.
-
D.
Ikebukuro
Ikebukuro is a major commercial and entertainment district in Tokyo known for its large train station, shopping complexes, and vibrant youth culture.
-
E.
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.
- 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_69b1f83bf9248190821580e4d1e76e4d |
completed | March 11, 2026, 11:18 p.m. |
Created at: March 1, 2026, 8:12 p.m.