Triple

T16281202
Position Surface form Disambiguated ID Type / Status
Subject qadiasker E395265 entity
Predicate relatedTo P37 FINISHED
Object kazasker
A kazasker was a high-ranking Ottoman military judge responsible for overseeing legal matters and the judiciary, particularly in the empire’s army and provinces.
 E1203296 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: kazasker | Statement: [qadiasker, relatedTo, kazasker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: kazasker
Context triple: [qadiasker, relatedTo, kazasker]
  • A. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • B. 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.
  • C. 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.
  • D. Kaz
    Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
  • E. KAZO
    KAZO is the ICAO airport code for Kalamazoo/Battle Creek International Airport in Michigan, United States.
  • 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: kazasker
Triple: [qadiasker, relatedTo, kazasker]
Generated description
A kazasker was a high-ranking Ottoman military judge responsible for overseeing legal matters and the judiciary, particularly in the empire’s army and provinces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: kazasker
Target entity description: A kazasker was a high-ranking Ottoman military judge responsible for overseeing legal matters and the judiciary, particularly in the empire’s army and provinces.
  • A. KAZ
    KAZ is the three-letter ISO 3166-1 alpha-3 country code assigned to Kazakhstan for international standardization and identification.
  • B. 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.
  • C. 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.
  • D. Kaz
    Kaz is a person known for working closely with Nik as a teammate, likely in a collaborative or competitive setting such as sports, gaming, or a professional project.
  • E. KAZO
    KAZO is the ICAO airport code for Kalamazoo/Battle Creek International Airport in Michigan, United States.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24910c6b881909ae5cc0908dd8eb2 completed April 17, 2026, 2:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0017c6b72081908a21e5099f463b62 completed May 10, 2026, 5:29 a.m.
NEDg Description generation batch_6a001876efb081909c0940ebcf265f15 completed May 10, 2026, 5:32 a.m.
NED2 Entity disambiguation (via description) batch_6a0018f6de84819087b8e97b0400c77d completed May 10, 2026, 5:34 a.m.
Created at: April 10, 2026, 5:05 a.m.