Triple

T1752306
Position Surface form Disambiguated ID Type / Status
Subject University of Cologne E38470 entity
Predicate abbreviation P43 FINISHED
Object UzK
UzK is the commonly used abbreviation for the University of Cologne, one of Germany’s largest and oldest universities.
 E196549 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: UzK | Statement: [University of Cologne, abbreviation, UzK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UzK
Context triple: [University of Cologne, abbreviation, UzK]
  • A. UZ
    UZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Uzbekistan.
  • B. KZ
    KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
  • C. USZ
    USZ is a major public teaching hospital in Zurich, Switzerland, affiliated with the University of Zurich and known for its advanced medical care and research.
  • D. ZUE
    ZUE is the railway station code for Zürich Hauptbahnhof, Switzerland’s largest and busiest train station and a major European rail hub.
  • E. ZS
    ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
  • 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: UzK
Triple: [University of Cologne, abbreviation, UzK]
Generated description
UzK is the commonly used abbreviation for the University of Cologne, one of Germany’s largest and oldest universities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UzK
Target entity description: UzK is the commonly used abbreviation for the University of Cologne, one of Germany’s largest and oldest universities.
  • A. UZ
    UZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Uzbekistan.
  • B. KZ
    KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
  • C. USZ
    USZ is a major public teaching hospital in Zurich, Switzerland, affiliated with the University of Zurich and known for its advanced medical care and research.
  • D. ZUE
    ZUE is the railway station code for Zürich Hauptbahnhof, Switzerland’s largest and busiest train station and a major European rail hub.
  • E. ZS
    ZS is the vehicle registration code assigned to cars registered in the Polish city of Szczecin.
  • 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_69a8862bdb2081908aefe831c8aa8017 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa641432d88190ab4254cb4c3ad402 completed March 6, 2026, 5:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0e625c48190a0fbda31010bdc5f completed March 8, 2026, 4:16 p.m.
NEDg Description generation batch_69ada1e13d408190b393c00c331125a2 completed March 8, 2026, 4:20 p.m.
NED2 Entity disambiguation (via description) batch_69ada292a34c8190a566c2909342ab27 completed March 8, 2026, 4:23 p.m.
Created at: March 4, 2026, 7:31 p.m.