Triple

T22240831
Position Surface form Disambiguated ID Type / Status
Subject Dalton Municipal Airport E549715 entity
Predicate hasFaaCode P420 FINISHED
Object DNN
DNN is the FAA location identifier assigned to Dalton Municipal Airport in Georgia, United States.
 E1526531 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: DNN | Statement: [Dalton Municipal Airport, hasFaaCode, DNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DNN
Context triple: [Dalton Municipal Airport, hasFaaCode, DNN]
  • A. DNN
    DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
  • B. FNN
    FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
  • C. FNN
    FNN is a Japanese commercial television news network centered around Fuji Television that provides national news programming through its affiliated stations.
  • D. NN
    NN is the postcode area in the United Kingdom that covers Northampton and surrounding parts of Northamptonshire.
  • E. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • 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: DNN
Triple: [Dalton Municipal Airport, hasFaaCode, DNN]
Generated description
DNN is the FAA location identifier assigned to Dalton Municipal Airport in Georgia, United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DNN
Target entity description: DNN is the FAA location identifier assigned to Dalton Municipal Airport in Georgia, United States.
  • A. DNN
    DNN is the stock ticker symbol for Denison Mines Corp., a Canadian uranium exploration and development company.
  • B. FNN
    FNN is the three-letter National Rail station code assigned to Farnborough North railway station in Hampshire, England.
  • C. FNN
    FNN is a Japanese commercial television news network centered around Fuji Television that provides national news programming through its affiliated stations.
  • D. NN
    NN is the postcode area in the United Kingdom that covers Northampton and surrounding parts of Northamptonshire.
  • E. deep feedforward networks
    Deep feedforward networks are a class of neural network architectures in which information flows in one direction through multiple layers to learn complex input–output mappings without recurrent connections.
  • 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.