Triple

T6335718
Position Surface form Disambiguated ID Type / Status
Subject S2 E142484 entity
Predicate connectsWith P37 FINISHED
Object Berlin tram E146051 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 tram | Statement: [S2, connectsWith, Berlin tram]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin tram
Context triple: [S2, connectsWith, Berlin tram]
  • A. Berlin tram chosen
    The Berlin tram is an extensive light rail network serving many districts of Germany’s capital, particularly in the eastern parts of the city, and forms a key component of its public transport system.
  • B. Potsdam tram network
    The Potsdam tram network is an urban light rail system serving the city of Potsdam, Germany, providing key public transportation links between its historic center, residential districts, and surrounding areas.
  • 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. Berlin Stadtbahn
    Berlin Stadtbahn is a major elevated east–west railway corridor in Berlin that carries S-Bahn and regional trains through the city’s central districts.
  • E. Gera tram network
    The Gera tram network is the urban light rail system serving the city of Gera in Thuringia, Germany, providing local public transport across the city and surrounding 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_69c008d4d8e88190ad301c05b08722ac completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0654a88a881908d5cb2aa7f22c4c7 completed March 22, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6042ab22c8190a7486049f45a546b completed March 27, 2026, 4:14 a.m.
Created at: March 22, 2026, 4:30 p.m.