Triple
T9874337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burley Park railway station |
E240035
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object | BUY |
E281344
|
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: BUY | Statement: [Burley Park railway station, hasStationCode, BUY]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BUY Context triple: [Burley Park railway station, hasStationCode, BUY]
-
A.
Buy
chosen
Buy is a historic town in Kostroma Oblast, Russia, known for its long-standing regional significance and traditional Russian provincial character.
-
B.
Bringsty
Bringsty is a small rural hamlet in Herefordshire, England, known for its scattered farms, countryside views, and proximity to the market town of Bromyard.
-
C.
Store
Store is the central NgRx state container that holds application state and enables reactive, unidirectional data flow in Angular applications.
-
D.
Zákupy
Zákupy is a small historic town in the Liberec Region of the Czech Republic, known for its Renaissance-Baroque chateau and its association with the Habsburg dynasty.
-
E.
BY
BY is the two-letter ISO 3166-1 alpha-2 country code assigned to Belarus.
- 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_69ca84e8a0788190b9061811d50fd554 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3f89fa081908b58956902c193cf |
completed | April 2, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e47515788190b6644021e187e771 |
completed | April 5, 2026, 4:26 a.m. |
Created at: March 30, 2026, 8:37 p.m.