Triple

T23015104
Position Surface form Disambiguated ID Type / Status
Subject Nikolassee station E573010 entity
Predicate railwayLine P848 FINISHED
Object S7 line 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: S7 line | Statement: [Nikolassee station, railwayLine, S7 line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: S7 line
Context triple: [Nikolassee station, railwayLine, S7 line]
  • A. S7 line
    The S7 line is a route of the Rhine-Main S-Bahn network serving the Frankfurt am Main region in Germany.
  • B. S7 line chosen
    The S7 line is a Berlin S-Bahn railway service that runs east–west across the city, connecting key districts and suburbs as part of the German capital’s urban transit network.
  • C. S7 line
    The S7 line is a suburban railway service of the Zürich S-Bahn network that connects the city of Zürich with surrounding municipalities along Lake Zürich.
  • D. S7 line
    The S7 line is a Vienna S-Bahn commuter rail route connecting the city center with Vienna International Airport and surrounding suburbs.
  • E. S7 Line
    The S7 Line is a suburban rapid transit line of the Nanjing Metro serving outlying districts to the south of Nanjing, China.
  • 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_69e245b764cc8190a51be76f1d9611e1 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f183e3c0e08190a7ac747b056ec3ca completed April 29, 2026, 4:06 a.m.
Created at: April 17, 2026, 3:51 p.m.