Triple

T19477088
Position Surface form Disambiguated ID Type / Status
Subject University of Worcester E487272 entity
Predicate hasViceChancellor P142 FINISHED
Object David Green
David Green is a British academic leader best known for serving as the Vice Chancellor of the University of Worcester, where he has overseen significant institutional growth and development.
 E1378370 NE FINISHED

How this triple was built (3 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.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: David Green
Context triple: [University of Worcester, hasViceChancellor, David Green]
  • A. David Green
    David Green is an American businessman and philanthropist best known as the founder of the arts-and-crafts retail chain Hobby Lobby.
  • B. David M. Green
    David M. Green is a distinguished figure in the field of acoustics recognized for his significant contributions with the prestigious ASA Gold Medal.
  • C. David Greene
    David Greene was a British-born film and television director known for his work on acclaimed TV movies and miniseries from the 1960s through the 1990s.
  • D. David Gest
    David Gest was an American television personality and music producer best known for his high-profile marriage to entertainer Liza Minnelli and his appearances on British reality TV.
  • E. Jay Graydon
    Jay Graydon is an American guitarist, songwriter, and Grammy-winning producer known for his sophisticated pop and jazz fusion work with artists such as Steely Dan, Al Jarreau, and Airplay.
  • 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: David Green
Triple: [University of Worcester, hasViceChancellor, David Green]
Generated description
David Green is a British academic leader best known for serving as the Vice Chancellor of the University of Worcester, where he has overseen significant institutional growth and development.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: David Green
Target entity description: David Green is a British academic leader best known for serving as the Vice Chancellor of the University of Worcester, where he has overseen significant institutional growth and development.
  • A. David Green
    David Green is an American businessman and philanthropist best known as the founder of the arts-and-crafts retail chain Hobby Lobby.
  • B. David M. Green
    David M. Green is a distinguished figure in the field of acoustics recognized for his significant contributions with the prestigious ASA Gold Medal.
  • C. David Greene
    David Greene was a British-born film and television director known for his work on acclaimed TV movies and miniseries from the 1960s through the 1990s.
  • D. David Gest
    David Gest was an American television personality and music producer best known for his high-profile marriage to entertainer Liza Minnelli and his appearances on British reality TV.
  • E. Jay Graydon
    Jay Graydon is an American guitarist, songwriter, and Grammy-winning producer known for his sophisticated pop and jazz fusion work with artists such as Steely Dan, Al Jarreau, and Airplay.
  • F. None of above. chosen

Provenance (4 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_69d8e8d924388190b847cb15bb3d0aff completed April 10, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a074053f7b48190a726ef5e285ed62a completed May 15, 2026, 3:48 p.m.
NEDg Description generation batch_6a07412a04d0819087c4dc7b5f2742ad completed May 15, 2026, 3:52 p.m.
NED2 Entity disambiguation (via description) batch_6a074197aa14819096a74aab7bca9e3c completed May 15, 2026, 3:53 p.m.
Created at: April 10, 2026, 1:39 p.m.