Triple

T25652662
Position Surface form Disambiguated ID Type / Status
Subject Rüttenen E643143 entity
Predicate district P2709 FINISHED
Object Solothurn District
Solothurn District is an administrative district in the canton of Solothurn in northwestern Switzerland, centered around the city of Solothurn and comprising several surrounding municipalities.
E2080101 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: Solothurn District | Statement: [Rüttenen, district, Solothurn District]
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: Solothurn District
Triple: [Rüttenen, district, Solothurn District]
Generated description
Solothurn District is an administrative district in the canton of Solothurn in northwestern Switzerland, centered around the city of Solothurn and comprising several surrounding municipalities.

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_69e77e7d8a848190a98d0162325fd780 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5faa9a8f881908223f599950cc005 completed May 2, 2026, 1:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a36ae27aa9881909fdfd5e384b4003d completed June 20, 2026, 3:13 p.m.
NEDg Description generation batch_6a36af0ecea8819092b60c42572f3865 completed June 20, 2026, 3:17 p.m.
NED2 Entity disambiguation (via description) batch_6a36afaee2b88190b603b07a7700efa2 completed June 20, 2026, 3:20 p.m.
Created at: April 21, 2026, 6:27 p.m.