Triple

T5376113
Position Surface form Disambiguated ID Type / Status
Subject Athens Ben Epps Airport E108963 entity
Predicate ICAOcode P419 FINISHED
Object KAHN
KAHN is the ICAO airport code for Athens Ben Epps Airport, a public airport serving Athens, Georgia, in the United States.
 E516375 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: KAHN | Statement: [Athens Ben Epps Airport, ICAOcode, KAHN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KAHN
Context triple: [Athens Ben Epps Airport, ICAOcode, KAHN]
  • A. Kahn
    Kahn is a surname most famously associated with Louis Kahn, the influential 20th-century architect known for his monumental and timeless modernist buildings.
  • B. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • C. KAN
    KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
  • 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: KAHN
Triple: [Athens Ben Epps Airport, ICAOcode, KAHN]
Generated description
KAHN is the ICAO airport code for Athens Ben Epps Airport, a public airport serving Athens, Georgia, in the United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KAHN
Target entity description: KAHN is the ICAO airport code for Athens Ben Epps Airport, a public airport serving Athens, Georgia, in the United States.
  • A. Kahn
    Kahn is a surname most famously associated with Louis Kahn, the influential 20th-century architect known for his monumental and timeless modernist buildings.
  • B. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • C. KAN
    KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
  • 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_69bd440c77948190aad2a5f39b7b80f5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd86b08bf881909fa2e42c977d807a completed March 20, 2026, 5:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf293f6458819091c32080782b56ed completed March 21, 2026, 11:26 p.m.
NEDg Description generation batch_69bf2a23ba1881909ddc549728bbc2d3 completed March 21, 2026, 11:30 p.m.
NED2 Entity disambiguation (via description) batch_69bf2e6d5f9081908327dff0058241f0 completed March 21, 2026, 11:49 p.m.
Created at: March 20, 2026, 2:03 p.m.