Triple

T26230412
Position Surface form Disambiguated ID Type / Status
Subject Darwen railway station E656019 entity
Predicate category P87 FINISHED
Object Northern franchise railway stations
Northern franchise railway stations are train stations in Northern England served and managed by the Northern rail operating company as part of its regional passenger network.
E1714701 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: Northern franchise railway stations | Statement: [Darwen railway station, category, Northern franchise railway stations]
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: Northern franchise railway stations
Triple: [Darwen railway station, category, Northern franchise railway stations]
Generated description
Northern franchise railway stations are train stations in Northern England served and managed by the Northern rail operating company as part of its regional passenger network.

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_69ee5b4b8b408190993da38c0067cc8d completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60d56b0388190826b97fa17dc97ce completed May 2, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a118598521c81909a8fb3378419cab1 completed May 23, 2026, 10:46 a.m.
NEDg Description generation batch_6a11865aaac881909aa388f473a6e5a3 completed May 23, 2026, 10:50 a.m.
NED2 Entity disambiguation (via description) batch_6a11873fe9708190a0ad2b27028b120a completed May 23, 2026, 10:53 a.m.
Created at: April 26, 2026, 8:59 p.m.