Triple

T3577217
Position Surface form Disambiguated ID Type / Status
Subject Kazan University E75716 entity
Predicate shortName P43 FINISHED
Object KFU
KFU is a major Russian research and educational institution officially known as Kazan (Volga Region) Federal University.
 E369003 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: KFU | Statement: [Kazan University, shortName, KFU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KFU
Context triple: [Kazan University, shortName, KFU]
  • A. KUF
    KUF is the IATA airport code for Kurumoch International Airport serving the Samara region in Russia.
  • B. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • C. KU
    KU is the commonly used abbreviation for Korea University, one of South Korea’s leading private research universities.
  • D. KU
    KU is the commonly used abbreviation for Kettering University, a private university in Flint, Michigan known for its strong engineering and cooperative education programs.
  • E. KU
    KU is the vehicle registration code assigned to the district of Kulmbach in the Upper Franconia region of Bavaria, Germany.
  • 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: KFU
Triple: [Kazan University, shortName, KFU]
Generated description
KFU is a major Russian research and educational institution officially known as Kazan (Volga Region) Federal University.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KFU
Target entity description: KFU is a major Russian research and educational institution officially known as Kazan (Volga Region) Federal University.
  • A. KUF
    KUF is the IATA airport code for Kurumoch International Airport serving the Samara region in Russia.
  • B. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • C. KU
    KU is the commonly used abbreviation for Kettering University, a private university in Flint, Michigan known for its strong engineering and cooperative education programs.
  • D. KU
    KU is the vehicle registration code assigned to the district of Kulmbach in the Upper Franconia region of Bavaria, Germany.
  • E. KU
    KU is the commonly used abbreviation for Korea University, one of South Korea’s leading private research universities.
  • 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_69ad85d5e3008190bdfe0bacdd1f5a1b completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc0dd3e048190a0c6666e13ead9cd completed March 8, 2026, 6:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bbc3d4e88190b18ed318c55594cc completed March 13, 2026, 7:24 a.m.
NEDg Description generation batch_69b3bd01ba7881909a0987b7d5dad4c2 completed March 13, 2026, 7:30 a.m.
NED2 Entity disambiguation (via description) batch_69b3f5b6e66c81908700d5f3df0a864d completed March 13, 2026, 11:32 a.m.
Created at: March 8, 2026, 3:21 p.m.