Triple

T2805654
Position Surface form Disambiguated ID Type / Status
Subject Großer Wannsee E54046 entity
Predicate accessibleBy P1017 FINISHED
Object S-Bahn Berlin 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: S-Bahn Berlin | Statement: [Großer Wannsee, accessibleBy, S-Bahn Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: S-Bahn Berlin
Context triple: [Großer Wannsee, accessibleBy, S-Bahn Berlin]
  • 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. S-Bahn
    The S-Bahn is a German urban and suburban rapid transit rail system that connects city centers with surrounding metropolitan regions.
  • 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. 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. S-Bahn Nuremberg
    S-Bahn Nuremberg is a regional suburban rail network serving Nuremberg and its surrounding metropolitan area 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_69ab49dcee188190b5c6eca9ae9e3469 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde1525888190b3c04e10043c67d6 completed March 7, 2026, 8:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc676d03081908986026dfe5fc852 completed March 10, 2026, 7:21 a.m.
Created at: March 6, 2026, 9:59 p.m.