Triple

T22240830
Position Surface form Disambiguated ID Type / Status
Subject Dalton Municipal Airport E549715 entity
Predicate hasIcaoCode P419 FINISHED
Object KDNN
KDNN is the ICAO airport code assigned to Dalton Municipal Airport in Dalton, Georgia, United States.
 E1526530 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: KDNN | Statement: [Dalton Municipal Airport, hasIcaoCode, KDNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KDNN
Context triple: [Dalton Municipal Airport, hasIcaoCode, KDNN]
  • A. DNN
    DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
  • B. KDDC
    KDDC is the ICAO airport code for Dodge City Regional Airport, a public airport serving Dodge City, Kansas.
  • C. KNN
    KNN (k-nearest neighbors) is a simple, non-parametric machine learning algorithm used for classification and regression by predicting labels based on the closest training examples in the feature space.
  • D. KNDS
    KNDS is a European defense industry holding company formed as a joint venture between Germany’s Krauss-Maffei Wegmann and France’s Nexter Systems, specializing in military land systems such as tanks and armored vehicles.
  • E. KDPU
    KDPU is a Ukrainian higher education institution specializing in teacher training and pedagogical studies, located in Kryvyi Rih.
  • 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: KDNN
Triple: [Dalton Municipal Airport, hasIcaoCode, KDNN]
Generated description
KDNN is the ICAO airport code assigned to Dalton Municipal Airport in Dalton, Georgia, United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KDNN
Target entity description: KDNN is the ICAO airport code assigned to Dalton Municipal Airport in Dalton, Georgia, United States.
  • A. DNN
    DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
  • B. KDDC
    KDDC is the ICAO airport code for Dodge City Regional Airport, a public airport serving Dodge City, Kansas.
  • C. KNN
    KNN (k-nearest neighbors) is a simple, non-parametric machine learning algorithm used for classification and regression by predicting labels based on the closest training examples in the feature space.
  • D. KNDS
    KNDS is a European defense industry holding company formed as a joint venture between Germany’s Krauss-Maffei Wegmann and France’s Nexter Systems, specializing in military land systems such as tanks and armored vehicles.
  • E. KDPU
    KDPU is a Ukrainian higher education institution specializing in teacher training and pedagogical studies, located in Kryvyi Rih.
  • 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_69e11e4102b881909cf47d3768e25c19 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f132140ed481909ab0d4022756a4ba completed April 28, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0ab658d0bc81909f2b828296b20068 completed May 18, 2026, 6:48 a.m.
NEDg Description generation batch_6a0ab6ec0b5c819091e5c71354fef0b6 completed May 18, 2026, 6:51 a.m.
NED2 Entity disambiguation (via description) batch_6a0ab7bb6fbc81909e54be3d1ae98cbf completed May 18, 2026, 6:54 a.m.
Created at: April 16, 2026, 8:38 p.m.