Triple

T6358399
Position Surface form Disambiguated ID Type / Status
Subject Berlin public transport network E143047 entity
Predicate hasMajorHub P164 FINISHED
Object Ostkreuz E578119 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: Ostkreuz | Statement: [Berlin public transport network, hasMajorHub, Ostkreuz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ostkreuz
Context triple: [Berlin public transport network, hasMajorHub, Ostkreuz]
  • A. Ostkreuz chosen
    Ostkreuz is one of Berlin’s busiest and most important S-Bahn interchange stations, serving as a major hub for multiple suburban rail lines.
  • B. Karlshorst
    Karlshorst is a district in Berlin, Germany, historically notable as the site where Nazi Germany signed its unconditional surrender to the Soviet Union in 1945 and for hosting key Soviet military institutions during the postwar period.
  • C. Frankfurter Kreuz
    Frankfurter Kreuz is one of Germany’s busiest and most important motorway interchanges, connecting major autobahns near Frankfurt in the Rhine-Main region.
  • D. Spandau
    Spandau is a western borough of Berlin, Germany, known for its historic old town, fortress, and role as an important residential and industrial district.
  • E. Sorpedamm
    Sorpedamm is a reservoir dam in North Rhine-Westphalia, Germany, primarily used for water supply, flood control, and recreation.
  • 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_69c008d7a9c4819098d647ec47776917 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c067f5bdd481909cf9db595ddb27df completed March 22, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69c62d5f134c8190817037ad933c4d2b completed March 27, 2026, 7:10 a.m.
Created at: March 22, 2026, 4:32 p.m.