Triple

T30479764
Position Surface form Disambiguated ID Type / Status
Subject Schipbeek E775547 entity
Predicate partOf P40 FINISHED
Object Gelderland river network
The Gelderland river network is an interconnected system of rivers and streams in the Dutch province of Gelderland that plays a key role in regional water management, ecology, and transport.
 E1918459 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: Gelderland river network | Statement: [Schipbeek, partOf, Gelderland river 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: Gelderland river network
Triple: [Schipbeek, partOf, Gelderland river network]
Generated description
The Gelderland river network is an interconnected system of rivers and streams in the Dutch province of Gelderland that plays a key role in regional water management, ecology, and transport.

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_69f22497341481909c21ba329fadaa6b completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6871c7b4081908b43fb58b04f8c13 completed May 2, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27be65032c8190a30217cff79030d3 completed June 9, 2026, 7:19 a.m.
NEDg Description generation batch_6a27c0b97fe081909ff0ced6bb50700a completed June 9, 2026, 7:28 a.m.
NED2 Entity disambiguation (via description) batch_6a27c11678a08190a6824da7bd381c8e completed June 9, 2026, 7:30 a.m.
Created at: April 29, 2026, 8:12 p.m.