Triple

T13489262
Position Surface form Disambiguated ID Type / Status
Subject S-Bahn line S1 E318589 entity
Predicate passesThrough P225 FINISHED
Object Berlin Mitte E167243 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 Mitte | Statement: [S-Bahn line S1, passesThrough, Berlin Mitte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin Mitte
Context triple: [S-Bahn line S1, passesThrough, Berlin Mitte]
  • A. Berlin-Mitte locality chosen
    Berlin-Mitte locality is a central urban district of Berlin known for its historic core, major government buildings, and many of the city’s most prominent cultural and tourist landmarks.
  • B. Prenzlauer Berg
    Prenzlauer Berg is a trendy, gentrified district in Berlin known for its historic architecture, vibrant café culture, and popular nightlife.
  • C. Bornheim Mitte
    Bornheim Mitte is a central public transit station in Frankfurt’s Bornheim district, serving as a key stop on the city’s U-Bahn network.
  • D. Berlin government district
    The Berlin government district is the central area of Germany’s capital that houses key federal institutions, including the Bundestag in the Reichstag building and the offices of the Chancellor.
  • E. Berlin Gesundbrunnen
    Berlin Gesundbrunnen is a major railway and transport hub in northern Berlin, serving regional, long-distance, and S-Bahn trains as well as local U-Bahn and bus connections.
  • 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_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf3cbe2081908c6792362c67c8f1 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75483a6f88190b3815fb8d97e65e4 completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:43 p.m.