Triple

T4033745
Position Surface form Disambiguated ID Type / Status
Subject Mallam Aminu Kano International Airport E83777 entity
Predicate IATAcode P418 FINISHED
Object KAN
KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
 E409277 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: KAN | Statement: [Mallam Aminu Kano International Airport, IATAcode, KAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KAN
Context triple: [Mallam Aminu Kano International Airport, IATAcode, KAN]
  • A. Kan
    Kan is a transliteration variant of the title and name "Khan," historically used across Central and South Asia for rulers and nobility.
  • B. KNA
    KNA is the three-letter ISO 3166-1 alpha-3 country code assigned to Saint Kitts and Nevis.
  • C. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • D. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • E. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • 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: KAN
Triple: [Mallam Aminu Kano International Airport, IATAcode, KAN]
Generated description
KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KAN
Target entity description: KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
  • A. Kan
    Kan is a transliteration variant of the title and name "Khan," historically used across Central and South Asia for rulers and nobility.
  • B. KNA
    KNA is the three-letter ISO 3166-1 alpha-3 country code assigned to Saint Kitts and Nevis.
  • C. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • D. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • E. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • 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_69aed92e29ac819080f7a98b594fec05 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb108fc0819080c8f41da2e558e0 completed March 9, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5563e11708190abc9ba55b1be43a5 completed March 14, 2026, 12:36 p.m.
NEDg Description generation batch_69b55a291d8c8190976e764011692ba0 completed March 14, 2026, 12:52 p.m.
NED2 Entity disambiguation (via description) batch_69b55a9ec7e88190bc5d165fd666f4b3 completed March 14, 2026, 12:54 p.m.
Created at: March 9, 2026, 3:36 p.m.