Triple

T12533643
Position Surface form Disambiguated ID Type / Status
Subject Greer E299630 entity
Predicate hasNotableBearer P458 FINISHED
Object Margaret Greer
Margaret Greer is a notable individual who shares the surname Greer and is recognized as a distinguished bearer of that name.
 E1178671 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: Margaret Greer | Statement: [Greer, hasNotableBearer, Margaret Greer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Margaret Greer
Context triple: [Greer, hasNotableBearer, Margaret Greer]
  • A. Margaret Sexton
    Margaret Sexton was the wife of U.S. Navy Rear Admiral William Thomas Sampson, a prominent figure in the Spanish–American War.
  • B. Margaret Welsh
    Margaret Welsh is an American actress known for her work in film, television, and theater.
  • C. Margaret Haley
    Margaret Haley was an influential American educator and labor activist who championed teachers' rights and helped pioneer the modern teachers' union movement.
  • D. Margaret Cox
    Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
  • E. Margaret Heidenry
    Margaret Heidenry is a screenwriter best known for her work on the animated Disney sequel "Cinderella III: A Twist in Time."
  • 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: Margaret Greer
Triple: [Greer, hasNotableBearer, Margaret Greer]
Generated description
Margaret Greer is a notable individual who shares the surname Greer and is recognized as a distinguished bearer of that name.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Margaret Greer
Target entity description: Margaret Greer is a notable individual who shares the surname Greer and is recognized as a distinguished bearer of that name.
  • A. Margaret Sexton
    Margaret Sexton was the wife of U.S. Navy Rear Admiral William Thomas Sampson, a prominent figure in the Spanish–American War.
  • B. Margaret Welsh
    Margaret Welsh is an American actress known for her work in film, television, and theater.
  • C. Margaret Haley
    Margaret Haley was an influential American educator and labor activist who championed teachers' rights and helped pioneer the modern teachers' union movement.
  • D. Margaret Cox
    Margaret Cox is known as the daughter of British physicist and science communicator Brian Cox.
  • E. Margaret Heidenry
    Margaret Heidenry is a screenwriter best known for her work on the animated Disney sequel "Cinderella III: A Twist in Time."
  • 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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9546b8fd48190ae90e80785b2e2d1 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff99742618819083bba63ce9f27895 completed May 9, 2026, 8:30 p.m.
NEDg Description generation batch_69ff9b3f6ef0819087ad4ffd2e85ec0e completed May 9, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_69ff9bebae208190a3a2f76ae9893238 completed May 9, 2026, 8:41 p.m.
Created at: April 8, 2026, 9:57 p.m.