Triple

T8160873
Position Surface form Disambiguated ID Type / Status
Subject Cologne University of Applied Sciences E190573 entity
Predicate affiliation P10 FINISHED
Object UAS7
UAS7 is a German alliance of leading universities of applied sciences focused on practice-oriented education, research, and international collaboration.
 E715321 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: UAS7 | Statement: [Cologne University of Applied Sciences, affiliation, UAS7]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UAS7
Context triple: [Cologne University of Applied Sciences, affiliation, UAS7]
  • A. UAS
    UAS is the common abbreviation for University Air Squadrons, UK-based Royal Air Force units that provide flying training and leadership development to university students.
  • B. UP-78
    UP-78 is the vehicle registration code assigned to motor vehicles registered in Kanpur, Uttar Pradesh, India.
  • C. UZS
    UZS is the official currency code for the Uzbekistani soʻm, the national currency of Uzbekistan.
  • D. ANKA UAV
    ANKA UAV is a Turkish-made medium-altitude long-endurance unmanned aerial vehicle designed primarily for intelligence, surveillance, and reconnaissance missions.
  • E. USSF-67
    USSF-67 is a classified U.S. Space Force national security mission launched aboard a SpaceX Falcon Heavy rocket.
  • 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: UAS7
Triple: [Cologne University of Applied Sciences, affiliation, UAS7]
Generated description
UAS7 is a German alliance of leading universities of applied sciences focused on practice-oriented education, research, and international collaboration.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UAS7
Target entity description: UAS7 is a German alliance of leading universities of applied sciences focused on practice-oriented education, research, and international collaboration.
  • A. UAS
    UAS is the common abbreviation for University Air Squadrons, UK-based Royal Air Force units that provide flying training and leadership development to university students.
  • B. UP-78
    UP-78 is the vehicle registration code assigned to motor vehicles registered in Kanpur, Uttar Pradesh, India.
  • C. UZS
    UZS is the official currency code for the Uzbekistani soʻm, the national currency of Uzbekistan.
  • D. ANKA UAV
    ANKA UAV is a Turkish-made medium-altitude long-endurance unmanned aerial vehicle designed primarily for intelligence, surveillance, and reconnaissance missions.
  • E. USSF-67
    USSF-67 is a classified U.S. Space Force national security mission launched aboard a SpaceX Falcon Heavy rocket.
  • 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_69ca82c0ef14819083713f4473dd847c completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb455559188190bf95d9d93bb76002 completed March 31, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbf2c22f0819085c686c005f49486 completed April 1, 2026, 6:46 a.m.
NEDg Description generation batch_69ccc24d568081908b3c94edd35f071c completed April 1, 2026, 6:59 a.m.
NED2 Entity disambiguation (via description) batch_69ccc394a5488190b48681f4781f1be6 completed April 1, 2026, 7:04 a.m.
Created at: March 30, 2026, 5:38 p.m.