Triple

T7001815
Position Surface form Disambiguated ID Type / Status
Subject River Reuss E162353 entity
Predicate flowsThrough P225 FINISHED
Object Bremgarten
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
 E653195 NE FINISHED

How this triple was built (4 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: Bremgarten | Statement: [River Reuss, flowsThrough, Bremgarten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bremgarten
Context triple: [River Reuss, flowsThrough, Bremgarten]
  • A. Grenchen
    Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
  • B. Kilchberg
    Kilchberg is a municipality on the shores of Lake Zurich in Switzerland, known for its scenic residential character and as the home of the Lindt & Sprüngli chocolate factory.
  • C. Walchwil
    Walchwil is a picturesque Swiss municipality in the canton of Zug, known for its scenic location on the eastern shore of Lake Zug and views of the surrounding Alps.
  • D. Bönigen
    Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
  • E. Attiswil
    Attiswil is a municipality in the canton of Bern in Switzerland, located in the Oberaargau region.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Bremgarten
Triple: [River Reuss, flowsThrough, Bremgarten]
Generated description
Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bremgarten
Target entity description: Bremgarten is a historic Swiss town in the canton of Aargau, known for its well-preserved medieval old town and scenic riverside setting.
  • A. Grenchen
    Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
  • B. Kilchberg
    Kilchberg is a municipality on the shores of Lake Zurich in Switzerland, known for its scenic residential character and as the home of the Lindt & Sprüngli chocolate factory.
  • C. Walchwil
    Walchwil is a picturesque Swiss municipality in the canton of Zug, known for its scenic location on the eastern shore of Lake Zug and views of the surrounding Alps.
  • D. Bönigen
    Bönigen is a Swiss village in the canton of Bern, known for its scenic location on the shore of Lake Brienz near Interlaken.
  • E. Attiswil
    Attiswil is a municipality in the canton of Bern in Switzerland, located in the Oberaargau region.
  • F. None of above. chosen

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_69c68857ffc08190857dc62cd5253777 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dc1115c48190a9363473ae21b6c1 completed March 27, 2026, 7:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7dafa68dc8190bc46ba9695a41b4c completed March 28, 2026, 1:43 p.m.
NEDg Description generation batch_69c7dc1469e08190a0b2b924884885e6 completed March 28, 2026, 1:48 p.m.
NED2 Entity disambiguation (via description) batch_69c7dca05ad081908e2036ba6c909c09 completed March 28, 2026, 1:50 p.m.
Created at: March 27, 2026, 2:33 p.m.