Triple

T15499688
Position Surface form Disambiguated ID Type / Status
Subject Danville Regional Airport E378916 entity
Predicate IATACode P418 FINISHED
Object DAN
DAN is the IATA airport code for Danville Regional Airport, a public airport serving Danville, Virginia, in the United States.
 E1160118 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: DAN | Statement: [Danville Regional Airport, IATACode, DAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DAN
Context triple: [Danville Regional Airport, IATACode, DAN]
  • A. DAN
    DAN is a neutron-detecting scientific instrument on NASA's Curiosity rover used to measure subsurface hydrogen and infer the presence of water on Mars.
  • B. DAN
    DAN is the station code for Dane Road tram stop on Greater Manchester's Metrolink light rail network in England.
  • C. Dan
    Dan is the protagonist of Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," a post-scarcity future resident of a reputation-based society centered around a Disney theme park.
  • D. Dan
    Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
  • E. Dan
    Dan, better known as the Duke of Zhou, was an influential early Zhou dynasty statesman and regent in ancient China renowned for consolidating royal power and shaping foundational political and ritual institutions.
  • 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: DAN
Triple: [Danville Regional Airport, IATACode, DAN]
Generated description
DAN is the IATA airport code for Danville Regional Airport, a public airport serving Danville, Virginia, in the United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DAN
Target entity description: DAN is the IATA airport code for Danville Regional Airport, a public airport serving Danville, Virginia, in the United States.
  • A. DAN
    DAN is a neutron-detecting scientific instrument on NASA's Curiosity rover used to measure subsurface hydrogen and infer the presence of water on Mars.
  • B. DAN
    DAN is the station code for Dane Road tram stop on Greater Manchester's Metrolink light rail network in England.
  • C. Dan
    Dan is the protagonist of Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," a post-scarcity future resident of a reputation-based society centered around a Disney theme park.
  • D. Dan
    Dan is a male given name commonly used in English-speaking countries, often as a short form of Daniel.
  • E. Dan
    Dan, better known as the Duke of Zhou, was an influential early Zhou dynasty statesman and regent in ancient China renowned for consolidating royal power and shaping foundational political and ritual institutions.
  • 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_69d85cd53a7c819080f5b9042c4c199e completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03fcb4e8c81908e4ab463e3ae252b completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3667a53c81908be789f99e580265 completed May 9, 2026, 1:28 p.m.
NEDg Description generation batch_69ff3744ba8c81909989864ba107b93b completed May 9, 2026, 1:31 p.m.
NED2 Entity disambiguation (via description) batch_69ff37ee94b081909309062b2d30ede5 completed May 9, 2026, 1:34 p.m.
Created at: April 10, 2026, 3:54 a.m.