Triple
T10814229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanno |
E255182
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Hidaka |
E679281
|
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: Hidaka | Statement: [Hanno, borderedBy, Hidaka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hidaka Context triple: [Hanno, borderedBy, Hidaka]
-
A.
Hidaka
chosen
Hidaka was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
-
B.
Iruma
Iruma is a city in Saitama Prefecture, Japan, known for its residential suburbs, Sayama Hills greenery, and tea cultivation.
-
C.
Aoyama
Aoyama is an upscale district in Tokyo known for its high-end fashion boutiques, modern architecture, and trendy cafes and galleries.
-
D.
Oiyama
Oiyama is the climactic final race of the Hakata Gion Yamakasa festival in Fukuoka, where teams dash through the streets carrying elaborately decorated floats.
-
E.
Tateyama
Tateyama is a coastal city in southern Chiba Prefecture, Japan, known for its mild climate, beaches, and views of Mount Fuji across Tokyo Bay.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733ece4488190b553a66c4b5188bc |
completed | April 9, 2026, 5:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6554d0b0081909cc031ff06b796c0 |
completed | May 2, 2026, 7:49 p.m. |
Created at: April 8, 2026, 9:18 p.m.