Triple
T3482707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ikebukuro |
E73529
|
entity |
| Predicate | hasCommercialComplex |
P16039
|
FINISHED |
| Object | Esola Ikebukuro |
E73529
|
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: Esola Ikebukuro | Statement: [Ikebukuro, hasCommercialComplex, Esola Ikebukuro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esola Ikebukuro Context triple: [Ikebukuro, hasCommercialComplex, Esola Ikebukuro]
-
A.
Ikebukuro
chosen
Ikebukuro is a major commercial and entertainment district in Tokyo known for its large train station, shopping complexes, and vibrant youth culture.
-
B.
Shiodome
Shiodome is a modern high-rise business and commercial district in Tokyo, Japan, known for housing major corporate headquarters, upscale hotels, and shopping complexes.
-
C.
Ueno
Ueno is a major district in Tokyo known for Ueno Park, its museums, zoo, and busy transportation hub.
-
D.
Toyonaka
Toyonaka is a suburban city in Japan’s Kansai region known for its residential neighborhoods, educational institutions, and proximity to central Osaka.
-
E.
千駄ヶ谷
千駄ヶ谷は、東京都渋谷区に位置し、新国立競技場や明治神宮外苑などが近接する住宅地兼文教・スポーツエリアです。
- 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_69ad85b3c9b08190857cae74c7f36da9 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adbb76b5188190bf8f8a3f646a7184 |
completed | March 8, 2026, 6:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bee04d0b0c81908876ff0d675b93ae |
completed | March 21, 2026, 6:15 p.m. |
Created at: March 8, 2026, 3:17 p.m.