Triple

T12909212
Position Surface form Disambiguated ID Type / Status
Subject Friedman Memorial Airport E308807 entity
Predicate ICAOcode P419 FINISHED
Object KSUN
KSUN is the ICAO airport code for Friedman Memorial Airport, a public airport serving the Sun Valley and Hailey area in Idaho, United States.
 E1009164 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: KSUN | Statement: [Friedman Memorial Airport, ICAOcode, KSUN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KSUN
Context triple: [Friedman Memorial Airport, ICAOcode, KSUN]
  • A. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • B. KU
    KU is a common abbreviation for the University of Karachi, a major public research university in Karachi, Pakistan.
  • 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 abbreviated name of Sweden’s parliamentary Committee on the Constitution, which oversees constitutional matters and scrutinizes government activities.
  • 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: KSUN
Triple: [Friedman Memorial Airport, ICAOcode, KSUN]
Generated description
KSUN is the ICAO airport code for Friedman Memorial Airport, a public airport serving the Sun Valley and Hailey area in Idaho, United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KSUN
Target entity description: KSUN is the ICAO airport code for Friedman Memorial Airport, a public airport serving the Sun Valley and Hailey area in Idaho, United States.
  • A. KU
    KU is a common abbreviation for Kyoto University, a prestigious national research university in Kyoto, Japan.
  • B. KU
    KU is a common abbreviation for the University of Karachi, a major public research university in Karachi, Pakistan.
  • 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 abbreviated name of Sweden’s parliamentary Committee on the Constitution, which oversees constitutional matters and scrutinizes government activities.
  • E. 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.
  • 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_69d7bdf92b588190acdf2a2291ac4590 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9719e584c81909be1ac1366effca0 completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6a5680d748190b8453793219bda8f completed May 3, 2026, 1:31 a.m.
NEDg Description generation batch_69f6a6f6b3348190b50560e747f78d62 completed May 3, 2026, 1:37 a.m.
NED2 Entity disambiguation (via description) batch_69f6a7c12ef8819095d2418d9999926a completed May 3, 2026, 1:41 a.m.
Created at: April 9, 2026, 5:41 p.m.