Triple

T14257402
Position Surface form Disambiguated ID Type / Status
Subject Mitte E353420 entity
Predicate contains P35 FINISHED
Object Berlin Hauptbahnhof E142491 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 Hauptbahnhof | Statement: [Mitte, contains, Berlin Hauptbahnhof]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin Hauptbahnhof
Context triple: [Mitte, contains, Berlin Hauptbahnhof]
  • A. Berlin Hauptbahnhof chosen
    Berlin Hauptbahnhof is the main railway station in Berlin and one of Europe’s largest and most important rail hubs, serving extensive regional, national, and international connections.
  • B. Berlin Ostbahnhof
    Berlin Ostbahnhof is one of Berlin’s main railway hubs, serving long-distance, regional, and S-Bahn trains in the eastern part of the city.
  • C. Berlin-Westkreuz station
    Berlin-Westkreuz station is a major Berlin S-Bahn interchange in the west of the city, serving as a key junction for multiple suburban rail lines.
  • D. Brandenburg Hauptbahnhof
    Brandenburg Hauptbahnhof is the main railway station serving the city of Brandenburg an der Havel in Germany, providing regional and long-distance rail connections.
  • E. Berlin station
    Berlin station was the original name of the main railway station in Kitchener, Ontario, reflecting the city’s former name of Berlin before it was changed during World War I.
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6352611c819090d062fe3079cd03 completed April 14, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d150b188190a0858ab94f81d9a8 completed May 8, 2026, 1:32 a.m.
Created at: April 10, 2026, 1:09 a.m.