Triple

T17935452
Position Surface form Disambiguated ID Type / Status
Subject University of Applied Sciences Zwickau E448451 entity
Predicate shortName P43 FINISHED
Object WHZ
WHZ is the abbreviated name of the University of Applied Sciences Zwickau, a German institution specializing in practice-oriented higher education and research.
 E1296719 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: WHZ | Statement: [University of Applied Sciences Zwickau, shortName, WHZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WHZ
Context triple: [University of Applied Sciences Zwickau, shortName, WHZ]
  • A. WHE
    WHE is the National Rail station code for Whalley railway station in Lancashire, England.
  • B. WHC
    WHC is the commonly used abbreviation for UNESCO’s World Heritage Centre, the body responsible for coordinating the World Heritage Convention and managing the World Heritage List.
  • C. WHC
    WHC is a major immersed-tube road tunnel in Hong Kong that runs beneath Victoria Harbour, connecting West Kowloon and Hong Kong Island.
  • D. WZ
    WZ is the IATA airline designator assigned to Red Wings Airlines, a Russian passenger carrier.
  • E. WHD
    WHD is the U.S. Department of Labor’s Wage and Hour Division, the federal agency responsible for enforcing minimum wage, overtime pay, child labor, and other key labor standards.
  • 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: WHZ
Triple: [University of Applied Sciences Zwickau, shortName, WHZ]
Generated description
WHZ is the abbreviated name of the University of Applied Sciences Zwickau, a German institution specializing in practice-oriented higher education and research.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WHZ
Target entity description: WHZ is the abbreviated name of the University of Applied Sciences Zwickau, a German institution specializing in practice-oriented higher education and research.
  • A. WHE
    WHE is the National Rail station code for Whalley railway station in Lancashire, England.
  • B. WHC
    WHC is the commonly used abbreviation for UNESCO’s World Heritage Centre, the body responsible for coordinating the World Heritage Convention and managing the World Heritage List.
  • C. WHC
    WHC is a major immersed-tube road tunnel in Hong Kong that runs beneath Victoria Harbour, connecting West Kowloon and Hong Kong Island.
  • D. WZ
    WZ is the IATA airline designator assigned to Red Wings Airlines, a Russian passenger carrier.
  • E. WHD
    WHD is the U.S. Department of Labor’s Wage and Hour Division, the federal agency responsible for enforcing minimum wage, overtime pay, child labor, and other key labor standards.
  • 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_69d8b9f79d14819095540856928f0e25 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4a55536e0819083dcfc4be71d447a completed April 19, 2026, 9:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a03213b71188190a74c401cdc371874 completed May 12, 2026, 12:46 p.m.
NEDg Description generation batch_6a032261fd1081909ba0b04db93d16da completed May 12, 2026, 12:51 p.m.
NED2 Entity disambiguation (via description) batch_6a03232d682c8190bb9b8773609c83c2 completed May 12, 2026, 12:55 p.m.
Created at: April 10, 2026, 10:21 a.m.