Triple

T27445286
Position Surface form Disambiguated ID Type / Status
Subject Stewarton E692259 entity
Predicate hasRailwayStation P918 FINISHED
Object Stewarton railway station
Stewarton railway station is a passenger rail stop in East Ayrshire, Scotland, serving the town of Stewarton on the line between Kilmarnock and Glasgow.
E1774259 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: Stewarton railway station | Statement: [Stewarton, hasRailwayStation, Stewarton railway station]
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: Stewarton railway station
Triple: [Stewarton, hasRailwayStation, Stewarton railway station]
Generated description
Stewarton railway station is a passenger rail stop in East Ayrshire, Scotland, serving the town of Stewarton on the line between Kilmarnock and Glasgow.

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_69ef5206c9248190b5975c2a7f9d229c completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d90df98819084ea88ad56d524af completed May 2, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbd840308190acc801fdee9c03bd completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bc771b0481909cca1c87c805f0de completed May 24, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a12bd3f12b481908606b8e373ff408a completed May 24, 2026, 8:56 a.m.
Created at: April 27, 2026, 12:46 p.m.