Triple
T34236460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bleasby railway station |
E878346
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object |
Nottinghamshire rail network
The Nottinghamshire rail network is the system of railway lines and services that connect towns and cities across the county of Nottinghamshire, integrating local stations into the wider UK rail infrastructure.
|
E2088647
|
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: Nottinghamshire rail network | Statement: [Bleasby railway station, region, Nottinghamshire rail network]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nottinghamshire rail network Triple: [Bleasby railway station, region, Nottinghamshire rail network]
Generated description
The Nottinghamshire rail network is the system of railway lines and services that connect towns and cities across the county of Nottinghamshire, integrating local stations into the wider UK rail infrastructure.
Provenance (5 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_69f349b22d8c819096b22df268382aa9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f710de62d48190a1ddc9bee7314a91 |
completed | May 3, 2026, 9:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36d5e68f7c8190bb05f40630102aac |
completed | June 20, 2026, 6:03 p.m. |
| NEDg | Description generation | batch_6a36ded74ec88190b2d25a00f2b30bc8 |
completed | June 20, 2026, 6:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a36e13bdf9c81908161cecded7bd29b |
completed | June 20, 2026, 6:51 p.m. |
Created at: May 1, 2026, 1:56 a.m.