Triple

T10665939
Position Surface form Disambiguated ID Type / Status
Subject Al-Farabi Kazakh National University E251357 entity
Predicate alternativeName P39 FINISHED
Object KazNU
KazNU is a leading public research university in Almaty, Kazakhstan, recognized as one of the country’s oldest and most prestigious higher education institutions.
 E876713 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: KazNU | Statement: [Al-Farabi Kazakh National University, alternativeName, KazNU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KazNU
Context triple: [Al-Farabi Kazakh National University, alternativeName, KazNU]
  • A. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • B. KAZO
    KAZO is the ICAO airport code for Kalamazoo/Battle Creek International Airport in Michigan, United States.
  • C. KAU
    KAU is the IATA airport code for Kauhava Air Base, a former military airfield in Kauhava, Finland.
  • D. Kaz
    Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
  • E. Kaz
    Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
  • 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: KazNU
Triple: [Al-Farabi Kazakh National University, alternativeName, KazNU]
Generated description
KazNU is a leading public research university in Almaty, Kazakhstan, recognized as one of the country’s oldest and most prestigious higher education institutions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KazNU
Target entity description: KazNU is a leading public research university in Almaty, Kazakhstan, recognized as one of the country’s oldest and most prestigious higher education institutions.
  • A. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • B. KAZO
    KAZO is the ICAO airport code for Kalamazoo/Battle Creek International Airport in Michigan, United States.
  • C. KAU
    KAU is the IATA airport code for Kauhava Air Base, a former military airfield in Kauhava, Finland.
  • D. Kaz
    Kaz is one of the futuristic, computer-generated Spheriks characters that served as an official mascot for the 2002 FIFA World Cup in South Korea and Japan.
  • E. Kaz
    Kaz is a central protagonist in the Disney XD series "Mighty Med," known as a comic book fan who becomes a sidekick and caretaker to real-life superheroes.
  • 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_69d6aa5b0d2881909584b20efc5877f0 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6f31f87748190bb44db8afa901763 completed April 9, 2026, 12:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69d97a9566e08190a92b49ce73963078 completed April 10, 2026, 10:32 p.m.
NEDg Description generation batch_69d97cc2b66c8190909a23927fbe3af5 completed April 10, 2026, 10:42 p.m.
NED2 Entity disambiguation (via description) batch_69d97e189800819087bf6af15b2370a2 completed April 10, 2026, 10:47 p.m.
Created at: April 8, 2026, 9:08 p.m.