Triple

T16759297
Position Surface form Disambiguated ID Type / Status
Subject AFLCMC E407297 entity
Predicate headquartersCity P62 FINISHED
Object Dayton
Dayton is a mid-sized city in southwestern Ohio known historically for its aviation heritage, manufacturing industry, and proximity to Wright-Patterson Air Force Base.
 E82485 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: Dayton | Statement: [AFLCMC, headquartersCity, Dayton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dayton
Context triple: [AFLCMC, headquartersCity, Dayton]
  • A. Dayton
    Dayton is a small town located in the state of Indiana in the United States.
  • B. Dayton
    Dayton is an unincorporated community and census-designated place located within South Brunswick Township in Middlesex County, New Jersey.
  • C. Dayton
    Dayton is a masculine given name of English origin used both as a first name and a surname.
  • D. Dayton
    Dayton is a small city in Minnesota known for its suburban-rural character and location within the Minneapolis–Saint Paul metropolitan area.
  • E. Dayton
    Dayton is a small city in southeastern Tennessee known historically as the site of the 1925 Scopes "Monkey" Trial.
  • 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: Dayton
Triple: [AFLCMC, headquartersCity, Dayton]
Generated description
Dayton is a mid-sized city in southwestern Ohio known historically for its aviation heritage, manufacturing industry, and proximity to Wright-Patterson Air Force Base.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dayton
Target entity description: Dayton is a mid-sized city in southwestern Ohio known historically for its aviation heritage, manufacturing industry, and proximity to Wright-Patterson Air Force Base.
  • A. Dayton chosen
    Dayton is a mid-sized city in southwestern Ohio known for its historic role in aviation, manufacturing, and research, including its close association with major U.S. Air Force installations.
  • B. Dayton
    Dayton is a small town located in the state of Indiana in the United States.
  • C. Dayton
    Dayton is a small city in southeastern Tennessee known historically as the site of the 1925 Scopes "Monkey" Trial.
  • D. Dayton
    Dayton is a small city in Minnesota known for its suburban-rural character and location within the Minneapolis–Saint Paul metropolitan area.
  • E. Dayton
    Dayton is an unincorporated community and census-designated place located within South Brunswick Township in Middlesex County, New Jersey.
  • 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_69d8839174188190909f190097207065 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3abeb3ab08190918f6bff686858be completed April 18, 2026, 4:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c2993f4c8190aecf29a4bcbf7b6a completed May 10, 2026, 5:38 p.m.
NEDg Description generation batch_6a00c41e33c481908694a0e62f630b29 completed May 10, 2026, 5:45 p.m.
NED2 Entity disambiguation (via description) batch_6a00c4771f2481909189bd5c2646caf2 completed May 10, 2026, 5:46 p.m.
Created at: April 10, 2026, 5:21 a.m.