Triple

T14191811
Position Surface form Disambiguated ID Type / Status
Subject Battle of Britain E351729 entity
Predicate editor P1954 FINISHED
Object Bert Bates
Bert Bates was a British film editor known for his work on notable mid-20th-century films, including war dramas such as "Battle of Britain."
 E1086582 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: Bert Bates | Statement: [Battle of Britain, editor, Bert Bates]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bert Bates
Context triple: [Battle of Britain, editor, Bert Bates]
  • A. James Gosling
    James Gosling is a Canadian computer scientist best known as the creator of the Java programming language.
  • B. Michael Hillegas
    Michael Hillegas was an American merchant and statesman who served as the first Treasurer of the United States during the Revolutionary era.
  • C. Andrew Hunt
    Andrew Hunt is a prominent software developer and author best known for co-writing the influential book "The Pragmatic Programmer."
  • D. Andrew Koenig
    Andrew Koenig was an American actor and activist best known for his role as Richard "Boner" Stabone on the television series "Growing Pains."
  • E. Michael Kölling
    Michael Kölling is a computer scientist and educator best known for his influential work on object-oriented programming education and the development of the BlueJ and Greenfoot learning environments.
  • 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: Bert Bates
Triple: [Battle of Britain, editor, Bert Bates]
Generated description
Bert Bates was a British film editor known for his work on notable mid-20th-century films, including war dramas such as "Battle of Britain."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bert Bates
Target entity description: Bert Bates was a British film editor known for his work on notable mid-20th-century films, including war dramas such as "Battle of Britain."
  • A. James Gosling
    James Gosling is a Canadian computer scientist best known as the creator of the Java programming language.
  • B. Michael Hillegas
    Michael Hillegas was an American merchant and statesman who served as the first Treasurer of the United States during the Revolutionary era.
  • C. Andrew Hunt
    Andrew Hunt is a prominent software developer and author best known for co-writing the influential book "The Pragmatic Programmer."
  • D. Andrew Koenig
    Andrew Koenig was an American actor and activist best known for his role as Richard "Boner" Stabone on the television series "Growing Pains."
  • E. Michael Kölling
    Michael Kölling is a computer scientist and educator best known for his influential work on object-oriented programming education and the development of the BlueJ and Greenfoot learning environments.
  • 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_69d827894ac0819097803e57f3227b23 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61df628c8190ba3f557e2128dce5 completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd1946eb68819096adf3c16a39818d completed May 7, 2026, 10:59 p.m.
NEDg Description generation batch_69fd1eed1008819088635be43fbb1439 completed May 7, 2026, 11:23 p.m.
NED2 Entity disambiguation (via description) batch_69fd1f7c5d208190bab5d57e931fd082 completed May 7, 2026, 11:25 p.m.
Created at: April 10, 2026, 1:04 a.m.