Triple

T8083198
Position Surface form Disambiguated ID Type / Status
Subject H. H. Munro E188666 entity
Predicate penName P3799 FINISHED
Object Saki
Saki is the pen name of British writer H. H. Munro, best known for his witty, darkly humorous short stories satirizing Edwardian society.
 E713443 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: Saki | Statement: [H. H. Munro, penName, Saki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Saki
Context triple: [H. H. Munro, penName, Saki]
  • A. Saki
    Saki is a prominent town in southwestern Nigeria known as a commercial and agricultural hub within Oyo State.
  • B. Michael Innes
    Michael Innes was the pen name of Scottish author J.I.M. Stewart, best known for his erudite and witty detective novels featuring Inspector John Appleby.
  • C. P. G. Wodehouse
    P. G. Wodehouse was an English author celebrated for his witty, farcical comic novels and stories, particularly those featuring Jeeves and Wooster.
  • D. Ann Firbank
    Ann Firbank is a British actress best known for her work in film, television, and theatre, including prominent roles in literary adaptations.
  • E. James Burrough
    James Burrough was an 18th-century English architect and academic at the University of Cambridge, known for helping shape the university’s neoclassical architectural style.
  • 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: Saki
Triple: [H. H. Munro, penName, Saki]
Generated description
Saki is the pen name of British writer H. H. Munro, best known for his witty, darkly humorous short stories satirizing Edwardian society.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Saki
Target entity description: Saki is the pen name of British writer H. H. Munro, best known for his witty, darkly humorous short stories satirizing Edwardian society.
  • A. Saki
    Saki is a prominent town in southwestern Nigeria known as a commercial and agricultural hub within Oyo State.
  • B. Michael Innes
    Michael Innes was the pen name of Scottish author J.I.M. Stewart, best known for his erudite and witty detective novels featuring Inspector John Appleby.
  • C. P. G. Wodehouse
    P. G. Wodehouse was an English author celebrated for his witty, farcical comic novels and stories, particularly those featuring Jeeves and Wooster.
  • D. Ann Firbank
    Ann Firbank is a British actress best known for her work in film, television, and theatre, including prominent roles in literary adaptations.
  • E. James Burrough
    James Burrough was an 18th-century English architect and academic at the University of Cambridge, known for helping shape the university’s neoclassical architectural style.
  • 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_69ca82b662e88190b9323daab8c28a21 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb415cb4688190920868317e77bbff completed March 31, 2026, 3:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc93eddef48190b5f499a5b52428c8 completed April 1, 2026, 3:41 a.m.
NEDg Description generation batch_69cc9557c6148190a759021b6add0a61 completed April 1, 2026, 3:47 a.m.
NED2 Entity disambiguation (via description) batch_69cc96a8bb688190a352de1798b380f1 completed April 1, 2026, 3:53 a.m.
Created at: March 30, 2026, 5:28 p.m.