Triple
T14219763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akiruno |
E352457
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Hinohara |
—
|
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: Hinohara | Statement: [Akiruno, borderedBy, Hinohara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hinohara Context triple: [Akiruno, borderedBy, Hinohara]
-
A.
Hinohara
chosen
Hinohara is a rural village in western Tokyo, Japan, known for its mountainous terrain, forests, and outdoor recreation areas.
-
B.
Ichihara
Ichihara is a coastal industrial city in Chiba Prefecture, Japan, known for its large petrochemical complexes and proximity to Tokyo Bay.
-
C.
Izuhara
Izuhara is the main town and administrative center of Tsushima Island in Nagasaki Prefecture, Japan.
-
D.
Higashikawa
Higashikawa is a town in Hokkaido, Japan, known as a gateway to the Daisetsuzan mountain range and for its scenic natural landscapes.
-
E.
Marunouchi
Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
- 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_69d8278a06e481908b5d6af0a8afe737 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de621258d4819085f358cd2cf109e4 |
completed | April 14, 2026, 3:49 p.m. |
Created at: April 10, 2026, 1:06 a.m.