Triple
T15947102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toyonaka |
E386712
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Ikeda |
E956391
|
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: Ikeda | Statement: [Toyonaka, borderedBy, Ikeda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ikeda Context triple: [Toyonaka, borderedBy, Ikeda]
-
A.
Ikeda
Ikeda is a common Japanese surname borne by numerous notable figures in politics, arts, sports, and academia.
-
B.
Ikeda
chosen
Ikeda is a city in Osaka Prefecture, Japan, known as a residential and industrial suburb within the Kansai region.
-
C.
Murayama
Murayama is a Japanese surname borne by various notable individuals across fields such as politics, science, and the arts.
-
D.
Ichikawa
Ichikawa is a city in Chiba Prefecture, Japan, located just east of Tokyo and known as a residential and commercial hub within the Greater Tokyo Area.
-
E.
Ichigaya
Ichigaya is a central Tokyo district known for its major railway station, government and educational institutions, and proximity to the Imperial Palace area.
- 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_69d86da882448190a82ea962fe343b79 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e156d1a4c08190afc325491ba38870 |
completed | April 16, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb5c0c8a481908aa7a40bca15e38e |
completed | May 9, 2026, 10:31 p.m. |
Created at: April 10, 2026, 4:53 a.m.