Triple

T5370987
Position Surface form Disambiguated ID Type / Status
Subject Volusia County E108848 entity
Predicate hasCity P316 FINISHED
Object Orange City
Orange City is a small municipality in central Florida known for its historic charm and proximity to natural springs and outdoor recreation areas.
 E515778 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: Orange City | Statement: [Volusia County, hasCity, Orange City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orange City
Context triple: [Volusia County, hasCity, Orange City]
  • A. Orange City
    Orange City is the popular nickname of Nagpur, a major city in Maharashtra, India, famed for its extensive orange cultivation and trade.
  • B. Orange City, Iowa
    Orange City, Iowa is a small northwestern Iowa community known for its Dutch heritage, annual Tulip Festival, and role as the cultural and economic hub of Sioux County.
  • C. Pasco
    Pasco is a city in southeastern Washington State that forms part of the Tri-Cities region along with Kennewick and Richland.
  • D. Lakeland
    Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
  • E. Ocala
    Ocala is a city in north-central Florida known for its thoroughbred horse farms and historic downtown.
  • 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: Orange City
Triple: [Volusia County, hasCity, Orange City]
Generated description
Orange City is a small municipality in central Florida known for its historic charm and proximity to natural springs and outdoor recreation areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orange City
Target entity description: Orange City is a small municipality in central Florida known for its historic charm and proximity to natural springs and outdoor recreation areas.
  • A. Orange City
    Orange City is the popular nickname of Nagpur, a major city in Maharashtra, India, famed for its extensive orange cultivation and trade.
  • B. Orange City, Iowa
    Orange City, Iowa is a small northwestern Iowa community known for its Dutch heritage, annual Tulip Festival, and role as the cultural and economic hub of Sioux County.
  • C. Pasco
    Pasco is a city in southeastern Washington State that forms part of the Tri-Cities region along with Kennewick and Richland.
  • D. Lakeland
    Lakeland is a residential neighborhood located within the city of College Park in Prince George's County, Maryland.
  • E. Ocala
    Ocala is a city in north-central Florida known for its thoroughbred horse farms and historic downtown.
  • 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_69bd440c77948190aad2a5f39b7b80f5 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd8688b7488190a57baedd52a11b1a completed March 20, 2026, 5:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf29307cc481908fa4d8b52711bbd4 completed March 21, 2026, 11:26 p.m.
NEDg Description generation batch_69bf29d11d2c819095ce493c8866f624 completed March 21, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_69bf2aa2a1688190bc41eb5e259d7d1f completed March 21, 2026, 11:32 p.m.
Created at: March 20, 2026, 2:02 p.m.