Triple
T24760321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kawanishi-Ikeda Station |
E619417
|
entity |
| Predicate | adjacentMunicipalityAccess |
P110090
|
FINISHED |
| Object | Ikeda, Osaka Prefecture |
—
|
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: Ikeda, Osaka Prefecture | Statement: [Kawanishi-Ikeda Station, adjacentMunicipalityAccess, Ikeda, Osaka Prefecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adjacentMunicipalityAccess Context triple: [Kawanishi-Ikeda Station, adjacentMunicipalityAccess, Ikeda, Osaka Prefecture]
-
A.
neighbouringMunicipality
Indicates that one municipality directly borders and is adjacent to another municipality.
-
B.
adjacentProvince
Indicates that two provinces share a common boundary and are directly next to each other geographically.
-
C.
neighboringBarangay
Indicates that two barangays are directly adjacent to each other, sharing a common boundary or border.
-
D.
hasAdjacentMunicipalitiesServed
chosen
Indicates that a municipality has neighboring municipalities that are also served by the same service, system, or administrative arrangement.
-
E.
hasCommunesNear
Indicates that one entity has communes located in its nearby geographic vicinity.
- F. None of above.
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_69e2fabbea94819092ed41348909622f |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 4:27 a.m.