Triple

T11270686
Position Surface form Disambiguated ID Type / Status
Subject Kyle Arrington E266802 entity
Predicate familyName P18 FINISHED
Object Arrington
Arrington is a surname most notably associated with individuals such as former NFL cornerback Kyle Arrington.
 E915198 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: Arrington | Statement: [Kyle Arrington, familyName, Arrington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arrington
Context triple: [Kyle Arrington, familyName, Arrington]
  • A. Warrenton
    Warrenton is a small historic town in Fauquier County that serves as a commercial and civic hub for the surrounding rural areas of Northern Virginia.
  • B. Wilmore
    Wilmore is a small city in central Kentucky known for its strong Christian academic community and as the home of Asbury University and Asbury Theological Seminary.
  • C. Wilmore
    Wilmore is an English-language surname borne by various notable individuals across fields such as entertainment, sports, and public service.
  • D. Burkley
    Burkley is a surname most notably associated with American character actor Dennis Burkley.
  • E. Braswell
    Braswell is the codename for an Intel low-power system-on-chip platform designed primarily for entry-level desktops and laptops.
  • 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: Arrington
Triple: [Kyle Arrington, familyName, Arrington]
Generated description
Arrington is a surname most notably associated with individuals such as former NFL cornerback Kyle Arrington.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arrington
Target entity description: Arrington is a surname most notably associated with individuals such as former NFL cornerback Kyle Arrington.
  • A. Warrenton
    Warrenton is a small historic town in Fauquier County that serves as a commercial and civic hub for the surrounding rural areas of Northern Virginia.
  • B. Wilmore
    Wilmore is a small city in central Kentucky known for its strong Christian academic community and as the home of Asbury University and Asbury Theological Seminary.
  • C. Wilmore
    Wilmore is an English-language surname borne by various notable individuals across fields such as entertainment, sports, and public service.
  • D. Burkley
    Burkley is a surname most notably associated with American character actor Dennis Burkley.
  • E. Braswell
    Braswell is the codename for an Intel low-power system-on-chip platform designed primarily for entry-level desktops and laptops.
  • 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_69d6aac8c2f48190ad0596f1f89f0470 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9506204819089dc0827483bd948 completed April 9, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4ccdf9e0c819098a921146e8d6e30 completed April 19, 2026, 12:38 p.m.
NEDg Description generation batch_69e4d9ed6a048190ae7476d44cee6a6e completed April 19, 2026, 1:34 p.m.
NED2 Entity disambiguation (via description) batch_69e4ddb1b4c8819087699bc73610c7f8 completed April 19, 2026, 1:50 p.m.
Created at: April 8, 2026, 9:31 p.m.