Triple

T14184422
Position Surface form Disambiguated ID Type / Status
Subject Federal Banking Supervisory Office E351537 entity
Predicate shortName P43 FINISHED
Object BAKred
BAKred was the abbreviated name of Germany’s former Federal Banking Supervisory Office, the national authority responsible for overseeing and regulating the banking sector before its functions were integrated into BaFin.
 E1084306 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: BAKred | Statement: [Federal Banking Supervisory Office, shortName, BAKred]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BAKred
Context triple: [Federal Banking Supervisory Office, shortName, BAKred]
  • A. BAK
    BAK is the National Rail station code used to identify Baker Street station in London’s rail network.
  • B. БАН
    БАН is the Bulgarian abbreviation for the Bulgarian Academy of Sciences, the leading national institution for scientific research in Bulgaria.
  • C. BNK
    BNK is the IATA airport code for Ballina Byron Gateway Airport, a regional airport serving the Ballina and Byron Bay areas in New South Wales, Australia.
  • D. BOK
    BOK is the station code for Berlin Ostkreuz, a major railway interchange in Berlin, Germany.
  • E. BOK
    BOK is the commonly used nickname for the BOK Center, a major multi-purpose arena in Tulsa, Oklahoma.
  • 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: BAKred
Triple: [Federal Banking Supervisory Office, shortName, BAKred]
Generated description
BAKred was the abbreviated name of Germany’s former Federal Banking Supervisory Office, the national authority responsible for overseeing and regulating the banking sector before its functions were integrated into BaFin.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BAKred
Target entity description: BAKred was the abbreviated name of Germany’s former Federal Banking Supervisory Office, the national authority responsible for overseeing and regulating the banking sector before its functions were integrated into BaFin.
  • A. BAK
    BAK is the National Rail station code used to identify Baker Street station in London’s rail network.
  • B. БАН
    БАН is the Bulgarian abbreviation for the Bulgarian Academy of Sciences, the leading national institution for scientific research in Bulgaria.
  • C. BNK
    BNK is the IATA airport code for Ballina Byron Gateway Airport, a regional airport serving the Ballina and Byron Bay areas in New South Wales, Australia.
  • D. BOK
    BOK is the station code for Berlin Ostkreuz, a major railway interchange in Berlin, Germany.
  • E. BOK
    BOK is the commonly used nickname for the BOK Center, a major multi-purpose arena in Tulsa, Oklahoma.
  • 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_69d8278834a08190b0f1784e58d7b99c completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61cc0a848190b660095972b1223b completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf81285c481908a5594bcb3304981 completed May 7, 2026, 8:37 p.m.
NEDg Description generation batch_69fd06a6e5d08190906cca66b2dcf565 completed May 7, 2026, 9:39 p.m.
NED2 Entity disambiguation (via description) batch_69fd07116e74819089aa9f75a11c6531 completed May 7, 2026, 9:41 p.m.
Created at: April 10, 2026, 1:03 a.m.