Triple

T9801788
Position Surface form Disambiguated ID Type / Status
Subject Mark Canton E237854 entity
Predicate familyName P18 FINISHED
Object Canton
Canton is a surname of English and French origin borne by various notable individuals across fields such as film production and politics.
 E821737 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: Canton | Statement: [Mark Canton, familyName, Canton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Canton
Context triple: [Mark Canton, familyName, Canton]
  • A. Canton
    Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • B. Canton
    Canton is a suburban town in Norfolk County, Massachusetts, located southwest of Boston and known for its residential character and local historic sites.
  • C. Canton
    Canton is a historic waterfront neighborhood in southeast Baltimore, Maryland, known for its revitalized harborfront, rowhouses, and vibrant bar and restaurant scene.
  • D. Canton
    Canton is a small New England town in Hartford County, Connecticut, known for its historic village centers and scenic Farmington River setting.
  • E. Canton
    Canton is a city in northeastern Ohio best known as the home of the Pro Football Hall of Fame.
  • 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: Canton
Triple: [Mark Canton, familyName, Canton]
Generated description
Canton is a surname of English and French origin borne by various notable individuals across fields such as film production and politics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Canton
Target entity description: Canton is a surname of English and French origin borne by various notable individuals across fields such as film production and politics.
  • A. Canton
    Canton is the historical Western name for Guangzhou, a major port city in southern China and the capital of Guangdong province.
  • B. Canton
    Canton is a suburban town in Norfolk County, Massachusetts, located southwest of Boston and known for its residential character and local historic sites.
  • C. Canton
    Canton is a small New England town in Hartford County, Connecticut, known for its historic village centers and scenic Farmington River setting.
  • D. Canton
    Canton is a historic waterfront neighborhood in southeast Baltimore, Maryland, known for its revitalized harborfront, rowhouses, and vibrant bar and restaurant scene.
  • E. Canton
    Canton is a city in northeastern Ohio best known as the home of the Pro Football Hall of Fame.
  • 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_69ca84dd4608819097ff4ed00feca280 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda62b41048190bcef70a7591830c6 completed April 1, 2026, 11:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c44edac48190a44fdfb858d0dbba completed April 5, 2026, 2:09 a.m.
NEDg Description generation batch_69d1c50af000819087d643cc41a6fcc8 completed April 5, 2026, 2:12 a.m.
NED2 Entity disambiguation (via description) batch_69d1c5d39b288190b276371591a86399 completed April 5, 2026, 2:15 a.m.
Created at: March 30, 2026, 8:29 p.m.