Triple

T25031789
Position Surface form Disambiguated ID Type / Status
Subject Apeldoorn station E626864 entity
Predicate railwayLine P848 FINISHED
Object Apeldoorn–Deventer railway
The Apeldoorn–Deventer railway is a Dutch rail line in the province of Gelderland that connects the cities of Apeldoorn and Deventer as part of the national railway network.
 E1758105 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: Apeldoorn–Deventer railway | Statement: [Apeldoorn station, railwayLine, Apeldoorn–Deventer railway]
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: Apeldoorn–Deventer railway
Triple: [Apeldoorn station, railwayLine, Apeldoorn–Deventer railway]
Generated description
The Apeldoorn–Deventer railway is a Dutch rail line in the province of Gelderland that connects the cities of Apeldoorn and Deventer as part of the national railway 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_69e2ff2a2c088190be513727ee8bfe78 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f44f6fcab081909470c94a5f519d79 completed May 1, 2026, 7 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1247cbc8808190b78d6f13de5cfe33 completed May 24, 2026, 12:35 a.m.
NEDg Description generation batch_6a1249b3e9888190b3bae29310007be4 completed May 24, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a124a849c3c81908f1b3acdbaed65f8 completed May 24, 2026, 12:47 a.m.
Created at: April 18, 2026, 6:07 a.m.