Triple

T7850466
Position Surface form Disambiguated ID Type / Status
Subject Michael Graydon E182037 entity
Predicate familyName P18 FINISHED
Object Graydon
Graydon is a surname of English and Irish origin borne by various notable individuals, including those in politics, sports, and the arts.
 E699973 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: Graydon | Statement: [Michael Graydon, familyName, Graydon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Graydon
Context triple: [Michael Graydon, familyName, Graydon]
  • A. Greer
    Greer is a surname most notably associated with Hal Greer, a Hall of Fame American basketball player.
  • B. Greer
    Greer is a small city in South Carolina known for its historic downtown, proximity to both Greenville and Spartanburg, and its role as a regional industrial and transportation hub.
  • C. Farlington
    Farlington is a small rural village in North Yorkshire, England, situated near the River Foss and known for its historic parish church and agricultural surroundings.
  • D. Shalden
    Shalden is a small rural village and civil parish in the East Hampshire district of Hampshire, England.
  • E. Grayling
    Grayling is a small city in northern Michigan known as a gateway to outdoor recreation in the surrounding forests, rivers, and lakes.
  • 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: Graydon
Triple: [Michael Graydon, familyName, Graydon]
Generated description
Graydon is a surname of English and Irish origin borne by various notable individuals, including those in politics, sports, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Graydon
Target entity description: Graydon is a surname of English and Irish origin borne by various notable individuals, including those in politics, sports, and the arts.
  • A. Greer
    Greer is a surname most notably associated with Hal Greer, a Hall of Fame American basketball player.
  • B. Greer
    Greer is a small city in South Carolina known for its historic downtown, proximity to both Greenville and Spartanburg, and its role as a regional industrial and transportation hub.
  • C. Farlington
    Farlington is a small rural village in North Yorkshire, England, situated near the River Foss and known for its historic parish church and agricultural surroundings.
  • D. Shalden
    Shalden is a small rural village and civil parish in the East Hampshire district of Hampshire, England.
  • E. Grayling
    Grayling is a small city in northern Michigan known as a gateway to outdoor recreation in the surrounding forests, rivers, and lakes.
  • 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_69ca82869ee08190b8f9040dbc2c0467 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb18eaac508190bf373b1d50b52e1e completed March 31, 2026, 12:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b0d95748190a202258214dda2ae completed March 31, 2026, 5:26 a.m.
NEDg Description generation batch_69cb762eab0881909c5035b3086dfdd9 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb801cc0c8190864d28e199eb5e67 completed March 31, 2026, 12:03 p.m.
Created at: March 30, 2026, 4:50 p.m.