Triple

T6514378
Position Surface form Disambiguated ID Type / Status
Subject S47 E148216 entity
Predicate partOf P40 FINISHED
Object Berlin S-Bahn E26713 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: Berlin S-Bahn | Statement: [S47, partOf, Berlin S-Bahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin S-Bahn
Context triple: [S47, partOf, Berlin S-Bahn]
  • A. Berlin S-Bahn chosen
    The Berlin S-Bahn is a rapid transit railway network serving Berlin and its surrounding areas, integrating suburban and urban rail services across the metropolitan region.
  • B. Berlin Stadtbahn
    Berlin Stadtbahn is a major elevated east–west railway corridor in Berlin that carries S-Bahn and regional trains through the city’s central districts.
  • C. Berlin U-Bahn
    The Berlin U-Bahn is the German capital’s extensive underground rapid transit system, forming a core part of its public transportation network.
  • D. Munich S-Bahn
    The Munich S-Bahn is a rapid transit and commuter rail network serving Munich and its surrounding metropolitan region in Bavaria, Germany.
  • E. Hamburg S-Bahn
    The Hamburg S-Bahn is a rapid transit and commuter rail network serving the city of Hamburg and its surrounding metropolitan region in northern Germany.
  • 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_69c687e68e748190baceb9298f32d3ed completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6ac0bea808190aebc2905fb53eeba completed March 27, 2026, 4:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e4f308048190a5c42022e3f9e855 completed March 28, 2026, 2:25 p.m.
Created at: March 27, 2026, 1:44 p.m.