Triple
T10634211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omiya |
E250535
|
entity |
| Predicate | connectedTo |
P37
|
FINISHED |
| Object | Utsunomiya |
E598240
|
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: Utsunomiya | Statement: [Omiya, connectedTo, Utsunomiya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Utsunomiya Context triple: [Omiya, connectedTo, Utsunomiya]
-
A.
Utsunomiya
chosen
Utsunomiya is a city in Tochigi Prefecture, Japan, known as a regional commercial center and for its specialty gyoza (dumplings).
-
B.
Matsudo
Matsudo is a city in Chiba Prefecture, Japan, located in the Greater Tokyo Area and functioning largely as a residential and commercial suburb of Tokyo.
-
C.
Omiya
Omiya is a major commercial and transportation hub in Saitama Prefecture, Japan, known for its busy railway station and urban center.
-
D.
Urayasu
Urayasu is a city in Chiba Prefecture, Japan, best known as the home of Tokyo Disney Resort and its associated entertainment and shopping complexes.
-
E.
Takasaki
Takasaki is a city in Japan’s Gunma Prefecture known for its Daruma doll production and as a regional commercial and transportation hub.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6dfab47bc819086684edc1b6dce74 |
completed | April 8, 2026, 11:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3cf630a8819094455fc45a815b83 |
completed | May 8, 2026, 1:31 a.m. |
Created at: April 8, 2026, 9:03 p.m.