Triple
T2048861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midland Main Line |
E45516
|
entity |
| Predicate | connectsStation |
P845
|
FINISHED |
| Object | Luton |
E51115
|
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: Luton | Statement: [Midland Main Line, connectsStation, Luton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luton Context triple: [Midland Main Line, connectsStation, Luton]
-
A.
Luton
chosen
Luton is a large town in Bedfordshire, England, known for its international airport and diverse urban population.
-
B.
Aylesbury
Aylesbury is a historic market town in southern England that serves as an important commercial and administrative center in Buckinghamshire.
-
C.
Hertford
Hertford is a historic market town and the county town of Hertfordshire in southern England.
-
D.
Milton Keynes
Milton Keynes is a large, planned new town in Buckinghamshire, England, known for its grid road system, modern architecture, and extensive green spaces.
-
E.
Dartford
Dartford is a historic market and industrial town in southeast England, situated on the River Darent and serving as a key commuter hub for London.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb98c70c48190beb98aad56d9daf1 |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2007386481908b46c7bc2db8e4dd |
completed | March 9, 2026, 1:19 a.m. |
Created at: March 4, 2026, 7:39 p.m.