Triple

T21513139
Position Surface form Disambiguated ID Type / Status
Subject U1 E530776 entity
Predicate connectsTo P845 FINISHED
Object S-Bahn Berlin NE NERFINISHED

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: [U1, connectsTo, S-Bahn Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: S-Bahn Berlin
Context triple: [U1, connectsTo, 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 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.
  • D. Hamburg S-Bahn
    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.
  • E. S-Bahn Ringbahn
    The S-Bahn Ringbahn is Berlin’s circular urban rail line that loops around the inner city, connecting numerous districts and major transport hubs.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c45c81f08190a6b8bbb70a45aae7 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e9ea8779c081908171c58d345d54ae completed April 23, 2026, 9:46 a.m.
Created at: April 16, 2026, 6:25 p.m.