Triple
T592794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M25 motorway |
E17312
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Kent |
E5977
|
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: Kent | Statement: [M25 motorway, passesThrough, Kent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kent Context triple: [M25 motorway, passesThrough, Kent]
-
A.
Kent
chosen
Kent is a county in southeastern England known for its historic towns, coastal landscapes, and nickname "the Garden of England."
-
B.
Rutland
Rutland is a small city in central Vermont known historically as a marble quarrying center and as a regional hub for commerce and outdoor recreation.
-
C.
York
York is a historic walled city in North Yorkshire, England, renowned for its medieval architecture, including York Minster, and its rich Roman and Viking heritage.
-
D.
York
York is a historic former municipality in Ontario, Canada, that is now part of the modern city of Toronto.
-
E.
Derbyshire
Derbyshire is a county in the East Midlands of England known for its rural landscapes, historic market towns, and much of the Peak District National Park.
- 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bbcaf5c81908de4e27096d3da13 |
completed | March 1, 2026, 8:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a51f33e59c8190b3593b8460411fba |
completed | March 2, 2026, 5:25 a.m. |
Created at: March 1, 2026, 7:33 p.m.