Triple

T6475204
Position Surface form Disambiguated ID Type / Status
Subject Berlin Ostbahnhof E146053 entity
Predicate formerName P65 FINISHED
Object Berlin Ost
Berlin Ost was the former name of Berlin Ostbahnhof, a major railway station in the eastern part of Berlin, Germany.
E614856 NE FINISHED

How this triple was built (4 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 Ost | Statement: [Berlin Ostbahnhof, formerName, Berlin Ost]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin Ost
Context triple: [Berlin Ostbahnhof, formerName, Berlin Ost]
  • A. East Berlin
    East Berlin was the Soviet-controlled eastern sector of Berlin that served as the capital of East Germany during the Cold War.
  • B. Berlin B
    Berlin B is one of the public transport fare zones in Berlin, covering the outer areas of the city beyond the central A zone.
  • C. Berlin
    Berlin is the capital and largest city of Germany, historically significant as a focal point of Cold War tensions and a major cultural, political, and economic center in Europe.
  • D. Berlin
    Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
  • E. Berlin
    Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Berlin Ost
Triple: [Berlin Ostbahnhof, formerName, Berlin Ost]
Generated description
Berlin Ost was the former name of Berlin Ostbahnhof, a major railway station in the eastern part of Berlin, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Berlin Ost
Target entity description: Berlin Ost was the former name of Berlin Ostbahnhof, a major railway station in the eastern part of Berlin, Germany.
  • A. East Berlin
    East Berlin was the Soviet-controlled eastern sector of Berlin that served as the capital of East Germany during the Cold War.
  • B. Berlin B
    Berlin B is one of the public transport fare zones in Berlin, covering the outer areas of the city beyond the central A zone.
  • C. Berlin
    Berlin is a charismatic, calculating, and morally ambiguous mastermind and heist leader in the Spanish television series "Money Heist" (La Casa de Papel).
  • D. Berlin
    Berlin is a major Ethereum network upgrade that introduced various gas cost optimizations and transaction processing improvements to enhance the blockchain’s efficiency and performance.
  • E. Berlin
    Berlin is a borough in Camden County, New Jersey, known as a suburban community within the Philadelphia metropolitan area.
  • F. None of above. chosen

Provenance (5 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_69c008fec7408190af7b146dc63d9750 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c06a341360819082f2b5496a1a68b0 completed March 22, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c70adfd6e48190badc31135f9b69a3 completed March 27, 2026, 10:55 p.m.
NEDg Description generation batch_69c70bb0714c819094e80a2dfc960c99 completed March 27, 2026, 10:58 p.m.
NED2 Entity disambiguation (via description) batch_69c70c51e0148190be64afb56690b34f completed March 27, 2026, 11:01 p.m.
Created at: March 22, 2026, 4:50 p.m.