Triple

T15816419
Position Surface form Disambiguated ID Type / Status
Subject Shreveport Downtown Airport E383489 entity
Predicate IATAcode P418 FINISHED
Object DTN
DTN is the IATA airport code for Shreveport Downtown Airport in Shreveport, Louisiana, United States.
 E617687 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: DTN | Statement: [Shreveport Downtown Airport, IATAcode, DTN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DTN
Context triple: [Shreveport Downtown Airport, IATAcode, DTN]
  • A. DTN
    DTN is the National Rail station code for Denton railway station in Greater Manchester, England.
  • B. DTL
    DTL is a type of linear accelerator structure that uses a series of drift tubes within an RF cavity to efficiently accelerate charged particle beams.
  • C. DTTA
    DTTA is the ICAO airport code for Tunis–Carthage International Airport, the main international gateway serving Tunis, the capital of Tunisia.
  • D. DTCL
    DTCL is the Office of Defense Trade Controls Licensing within the U.S. Department of State that reviews and authorizes exports of defense articles and services under the International Traffic in Arms Regulations (ITAR).
  • E. DTK
    DTK is Apple’s Intel-based Developer Transition Kit Mac mini-style prototype used to help developers prepare their apps for the company’s transition to Apple Silicon.
  • 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: DTN
Triple: [Shreveport Downtown Airport, IATAcode, DTN]
Generated description
DTN is the IATA airport code for Shreveport Downtown Airport in Shreveport, Louisiana, United States.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DTN
Target entity description: DTN is the IATA airport code for Shreveport Downtown Airport in Shreveport, Louisiana, United States.
  • A. DTN chosen
    DTN is the National Rail station code for Denton railway station in Greater Manchester, England.
  • B. DTL
    DTL is a type of linear accelerator structure that uses a series of drift tubes within an RF cavity to efficiently accelerate charged particle beams.
  • C. DTTA
    DTTA is the ICAO airport code for Tunis–Carthage International Airport, the main international gateway serving Tunis, the capital of Tunisia.
  • D. DTCL
    DTCL is the Office of Defense Trade Controls Licensing within the U.S. Department of State that reviews and authorizes exports of defense articles and services under the International Traffic in Arms Regulations (ITAR).
  • E. DTK
    DTK is Apple’s Intel-based Developer Transition Kit Mac mini-style prototype used to help developers prepare their apps for the company’s transition to Apple Silicon.
  • F. None of above.

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_69d86da2858c819090cc8481e7207b6e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0c4a306a48190840adc49df2c26c5 completed April 16, 2026, 11:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff99959f048190ae24a072387ec233 completed May 9, 2026, 8:31 p.m.
NEDg Description generation batch_69ff9ad6b29081909ff2abb2c4d866a4 completed May 9, 2026, 8:36 p.m.
NED2 Entity disambiguation (via description) batch_69ff9b443280819088dbf18f7c57406b completed May 9, 2026, 8:38 p.m.
Created at: April 10, 2026, 4:49 a.m.