Triple

T7839361
Position Surface form Disambiguated ID Type / Status
Subject Sir John Tusa E181765 entity
Predicate name P16 FINISHED
Object John Tusa
John Tusa is a British arts administrator, broadcaster, and former managing director of the BBC World Service and the Barbican Centre.
 E698849 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: John Tusa | Statement: [Sir John Tusa, name, John Tusa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Tusa
Context triple: [Sir John Tusa, name, John Tusa]
  • A. Jim Cavanaugh
    Jim Cavanaugh is an American businessman and aviation enthusiast best known as the founder of the Cavanaugh Flight Museum, which preserves and displays historic aircraft.
  • B. Jason Dooley
    Jason Dooley is a musician best known as a performer associated with the band Born of You.
  • C. Tom Tucker
    Tom Tucker is a fictional, mustachioed news anchor on the animated television series "Family Guy," known for his eccentric on-air persona and deadpan delivery.
  • D. Tony DiCicco
    Tony DiCicco was an American soccer coach best known for leading the U.S. women’s national team to victory in the 1996 Olympics and the 1999 FIFA Women’s World Cup.
  • E. Ernest Pagano
    Ernest Pagano was an American screenwriter best known for his work on Hollywood musical comedies in the 1930s and 1940s.
  • 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: John Tusa
Triple: [Sir John Tusa, name, John Tusa]
Generated description
John Tusa is a British arts administrator, broadcaster, and former managing director of the BBC World Service and the Barbican Centre.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Tusa
Target entity description: John Tusa is a British arts administrator, broadcaster, and former managing director of the BBC World Service and the Barbican Centre.
  • A. Jim Cavanaugh
    Jim Cavanaugh is an American businessman and aviation enthusiast best known as the founder of the Cavanaugh Flight Museum, which preserves and displays historic aircraft.
  • B. Jason Dooley
    Jason Dooley is a musician best known as a performer associated with the band Born of You.
  • C. Tom Tucker
    Tom Tucker is a fictional, mustachioed news anchor on the animated television series "Family Guy," known for his eccentric on-air persona and deadpan delivery.
  • D. Tony DiCicco
    Tony DiCicco was an American soccer coach best known for leading the U.S. women’s national team to victory in the 1996 Olympics and the 1999 FIFA Women’s World Cup.
  • E. Ernest Pagano
    Ernest Pagano was an American screenwriter best known for his work on Hollywood musical comedies in the 1930s and 1940s.
  • 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_69ca8284a25c8190a1a20afad30da792 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb14c4680481908628d22bbe4842f4 completed March 31, 2026, 12:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5abff2a88190a2f988b8b041ebb0 completed March 31, 2026, 5:25 a.m.
NEDg Description generation batch_69cb762dd8348190bf74be4e7f5df1e7 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb24068908190977b266366e5ceea completed March 31, 2026, 11:38 a.m.
Created at: March 30, 2026, 4:47 p.m.