Triple
T9301670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heald Green railway station |
E223776
|
entity |
| Predicate | servesDirection |
P12959
|
FINISHED |
| Object | Wilmslow |
E16562
|
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: Wilmslow | Statement: [Heald Green railway station, servesDirection, Wilmslow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wilmslow Context triple: [Heald Green railway station, servesDirection, Wilmslow]
-
A.
Wilmslow
chosen
Wilmslow is an affluent town in Cheshire, England, known for its prosperous residential areas and location within the Greater Manchester commuter belt.
-
B.
Rothwell
Rothwell is a town in Northamptonshire, England, situated near Kettering and known for its historic market heritage.
-
C.
Northwich
Northwich is a historic market and industrial town in Cheshire, England, traditionally known for its salt mining and chemical industries.
-
D.
Chorleywood
Chorleywood is a commuter village in South East England, known for its green spaces and location on the Metropolitan line within the London commuter belt.
-
E.
Daresbury
Daresbury is a village in Cheshire, England, best known as the birthplace of author Lewis Carroll and for its nearby national science and technology facilities.
- 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_69ca8424d0f08190831e2e93c6533aeb |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd08d1d954819098be177addafa406 |
completed | April 1, 2026, noon |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c77b8bf481909c5abf6523f75b2c |
completed | April 4, 2026, 8:10 a.m. |
Created at: March 30, 2026, 7:36 p.m.