Triple

T11041549
Position Surface form Disambiguated ID Type / Status
Subject Reeperbahn S-Bahn station E261029 entity
Predicate partOf P40 FINISHED
Object Hamburg S-Bahn E516213 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: Hamburg S-Bahn | Statement: [Reeperbahn S-Bahn station, partOf, Hamburg S-Bahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hamburg S-Bahn
Context triple: [Reeperbahn S-Bahn station, partOf, Hamburg S-Bahn]
  • A. Hamburg S-Bahn chosen
    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.
  • B. Hamburg U-Bahn
    The Hamburg U-Bahn is the rapid transit metro system serving the city of Hamburg, Germany, and its surrounding areas.
  • C. Berlin S-Bahn
    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.
  • D. Rhine-Ruhr S-Bahn
    The Rhine-Ruhr S-Bahn is a regional rapid transit network serving the densely populated Rhine-Ruhr metropolitan area in western Germany, connecting major cities such as Duisburg, Düsseldorf, Essen, and Dortmund.
  • E. 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.
  • 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_69d6aa979bdc8190bf0e79104cc098c1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7980050948190ae7b187da5b776ca completed April 9, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3c846d9f08190943d457ff6da6a9f completed April 18, 2026, 6:07 p.m.
Created at: April 8, 2026, 9:26 p.m.