Triple

T6605900
Position Surface form Disambiguated ID Type / Status
Subject Berlin Ostkreuz E149116 entity
Predicate railwayStationCode P1289 FINISHED
Object BOK
BOK is the station code for Berlin Ostkreuz, a major railway interchange in Berlin, Germany.
 E601837 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: BOK | Statement: [Berlin Ostkreuz, railwayStationCode, BOK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BOK
Context triple: [Berlin Ostkreuz, railwayStationCode, BOK]
  • A. BOK
    BOK is the commonly used abbreviation for the Bank of Korea, South Korea’s central bank responsible for monetary policy and financial stability.
  • B. BOL
    BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
  • C. Bokn
    Bokn is a small island municipality in southwestern Norway known for its coastal landscape and location in Rogaland county.
  • D. BOCHK
    BOCHK is the commonly used abbreviation for Bank of China (Hong Kong), a major commercial banking group based in Hong Kong.
  • E. BUK
    BUK is a major Nigerian federal university located in Kano, known for its wide range of academic programs and research activities.
  • 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: BOK
Triple: [Berlin Ostkreuz, railwayStationCode, BOK]
Generated description
BOK is the station code for Berlin Ostkreuz, a major railway interchange in Berlin, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BOK
Target entity description: BOK is the station code for Berlin Ostkreuz, a major railway interchange in Berlin, Germany.
  • A. BOK
    BOK is the commonly used abbreviation for the Bank of Korea, South Korea’s central bank responsible for monetary policy and financial stability.
  • B. BOL
    BOL is the three-letter ISO 3166-1 alpha-3 country code assigned to Bolivia.
  • C. Bokn
    Bokn is a small island municipality in southwestern Norway known for its coastal landscape and location in Rogaland county.
  • D. BOCHK
    BOCHK is the commonly used abbreviation for Bank of China (Hong Kong), a major commercial banking group based in Hong Kong.
  • E. BUK
    BUK is a major Nigerian federal university located in Kano, known for its wide range of academic programs and research activities.
  • 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_69c687eaa7508190bb58ce2aa02039b3 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af143d5c8190b62602602510b1cb completed March 27, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cbce25b481908d600d38d3c5b871 completed March 27, 2026, 6:26 p.m.
NEDg Description generation batch_69c6cd09753c81909df166156ffbf82a completed March 27, 2026, 6:31 p.m.
NED2 Entity disambiguation (via description) batch_69c6ce9ba47c819091496c87117e7a03 completed March 27, 2026, 6:38 p.m.
Created at: March 27, 2026, 1:56 p.m.