Triple

T4060913
Position Surface form Disambiguated ID Type / Status
Subject Innsbrucker Platz station E86207 entity
Predicate railwaySystem P522 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: [Innsbrucker Platz station, railwaySystem, Berlin S-Bahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin S-Bahn
Context triple: [Innsbrucker Platz station, railwaySystem, 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 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.
  • C. S-Bahn
    The S-Bahn is a German urban and suburban rapid transit rail system that connects city centers with surrounding metropolitan regions.
  • D. Frankfurt U-Bahn
    The Frankfurt U-Bahn is the rapid transit system serving Frankfurt am Main, Germany, forming a core part of the city's public transportation network with multiple underground and surface lines.
  • E. Munich U-Bahn
    The Munich U-Bahn is the German city's rapid transit metro system, forming a core part of its public transportation network with multiple underground lines serving urban and suburban areas.
  • 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_69aed93c69208190a4efac0efe3cd69b completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbd4acb0819093bcffcd05ed8e6f completed March 9, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b6721847ec8190bc4307ee958d1096 completed March 15, 2026, 8:47 a.m.
Created at: March 9, 2026, 3:38 p.m.