Triple

T6510754
Position Surface form Disambiguated ID Type / Status
Subject Katharine Tait E150124 entity
Predicate relative P37 FINISHED
Object Kate Russell
Kate Russell is a British technology journalist and television presenter best known for her work on the BBC’s technology program "Click."
 E610044 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: Kate Russell | Statement: [Katharine Tait, relative, Kate Russell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kate Russell
Context triple: [Katharine Tait, relative, Kate Russell]
  • A. Katherine Russell
    Katherine Russell is an American woman best known as the widow of Boston Marathon bomber Tamerlan Tsarnaev, who came under public scrutiny following the 2013 attack.
  • B. Tessa Ross
    Tessa Ross is a prominent British film and television producer known for backing acclaimed, often auteur-driven projects across UK cinema and high-end TV drama.
  • C. Kate Williams
    Kate Williams is a British historian, author, and television presenter known for her works on royal history and biography.
  • D. Kate Lynch
    Kate Lynch is a Canadian actress best known for her role in the 1979 comedy film "Meatballs."
  • E. Katherine Wilkinson
    Katherine Wilkinson is a climate strategist, author, and speaker known for her work on solutions-focused climate communication and leadership, including co-editing the influential book "All We Can Save."
  • 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: Kate Russell
Triple: [Katharine Tait, relative, Kate Russell]
Generated description
Kate Russell is a British technology journalist and television presenter best known for her work on the BBC’s technology program "Click."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kate Russell
Target entity description: Kate Russell is a British technology journalist and television presenter best known for her work on the BBC’s technology program "Click."
  • A. Katherine Russell
    Katherine Russell is an American woman best known as the widow of Boston Marathon bomber Tamerlan Tsarnaev, who came under public scrutiny following the 2013 attack.
  • B. Tessa Ross
    Tessa Ross is a prominent British film and television producer known for backing acclaimed, often auteur-driven projects across UK cinema and high-end TV drama.
  • C. Kate Williams
    Kate Williams is a British historian, author, and television presenter known for her works on royal history and biography.
  • D. Kate Lynch
    Kate Lynch is a Canadian actress best known for her role in the 1979 comedy film "Meatballs."
  • E. Katherine Wilkinson
    Katherine Wilkinson is a climate strategist, author, and speaker known for her work on solutions-focused climate communication and leadership, including co-editing the influential book "All We Can Save."
  • 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_69c687ef291081909d437f035eef1cda completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c69f3ad7d081909162f1a625fc52b1 completed March 27, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6eeca8ab88190aa8464671bdde2ec completed March 27, 2026, 8:55 p.m.
NEDg Description generation batch_69c6f09ea58c8190bfd8a183581b5a5a completed March 27, 2026, 9:03 p.m.
NED2 Entity disambiguation (via description) batch_69c6f1a0935881908afc30ce76bdf76f completed March 27, 2026, 9:07 p.m.
Created at: March 27, 2026, 1:43 p.m.