Triple
T16045772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miyagino-ku |
E389213
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Wakabayashi-ku |
—
|
NE NERFINISHED |
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: Wakabayashi-ku | Statement: [Miyagino-ku, borderedBy, Wakabayashi-ku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wakabayashi-ku Context triple: [Miyagino-ku, borderedBy, Wakabayashi-ku]
-
A.
Wakabayashi-ku
chosen
Wakabayashi-ku is one of the administrative wards of Sendai, Japan, encompassing residential, commercial, and coastal areas in the eastern part of the city.
-
B.
Wakamatsu-ku
Wakamatsu-ku is one of the seven wards of Kitakyushu in Fukuoka Prefecture, Japan, known for its coastal location and industrial heritage.
-
C.
Iwatsuki-ku
Iwatsuki-ku is a ward of Saitama City in Saitama Prefecture, Japan, known historically for its traditional doll-making industry.
-
D.
Nakahara-ku
Nakahara-ku is one of the administrative wards of Kawasaki City in Kanagawa Prefecture, Japan, known as a residential and commercial area within the Greater Tokyo metropolitan region.
-
E.
Tsurumi-ku
Tsurumi-ku is one of the wards of Yokohama in Kanagawa Prefecture, Japan, known as an industrial and residential area with several universities and cultural sites.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1835dd9a0819087e362cf5770232a |
completed | April 17, 2026, 12:48 a.m. |
Created at: April 10, 2026, 4:56 a.m.