Triple

T6015358
Position Surface form Disambiguated ID Type / Status
Subject Geneva–Lausanne–Bern–Zurich main line E133936 entity
Predicate connects P390 FINISHED
Object Bern E18380 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: Bern | Statement: [Geneva–Lausanne–Bern–Zurich main line, connects, Bern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bern
Context triple: [Geneva–Lausanne–Bern–Zurich main line, connects, Bern]
  • A. Bern chosen
    Bern is the capital city of Switzerland, known for its well-preserved medieval old town and role as a political and cultural center.
  • B. Bron
    Bron is a suburban commune in eastern France that forms part of the metropolitan area of Lyon.
  • C. Canton
    Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • D. Canton
    Canton is a historic waterfront neighborhood in southeast Baltimore, Maryland, known for its revitalized harborfront, rowhouses, and vibrant bar and restaurant scene.
  • E. Canton
    Canton is a suburban town in Norfolk County, Massachusetts, located southwest of Boston and known for its residential character and local historic sites.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69c0087361a48190905c6b55969852b8 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c04f80ec108190ae9711727debb061 completed March 22, 2026, 8:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c108ade3948190994fbaa2a5539216 completed March 23, 2026, 9:32 a.m.
Created at: March 22, 2026, 4:06 p.m.