Triple
T9128920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charnwood Forest |
E219032
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Coalville |
E305799
|
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: Coalville | Statement: [Charnwood Forest, hasNearbySettlement, Coalville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coalville Context triple: [Charnwood Forest, hasNearbySettlement, Coalville]
-
A.
Coalville
chosen
Coalville is an industrial town in Leicestershire, England, historically known for its coal mining and manufacturing heritage.
-
B.
Bearbrook
Bearbrook is a small river or stream running through the town of Aylesbury in Buckinghamshire, England.
-
C.
Kineton
Kineton is a village in Warwickshire, England, known for its proximity to the historic Edgehill battlefield of the English Civil War.
-
D.
Wolverley
Wolverley is a village in Worcestershire, England, known as the birthplace of the renowned 18th-century printer and typographer John Baskerville.
-
E.
Deatsville
Deatsville is a small town in central Alabama known for its rural character and proximity to the Montgomery metropolitan area.
- 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8cc85b081908ee80db0e3b7df15 |
completed | April 1, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d030a934988190976aa359f9f902c3 |
completed | April 3, 2026, 9:27 p.m. |
Created at: March 30, 2026, 7:18 p.m.