Triple

T17201733
Position Surface form Disambiguated ID Type / Status
Subject Sylvia Plachy E417489 entity
Predicate employer P7 FINISHED
Object Granta E836748 NE FINISHED

How this triple was built (2 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: Granta | Statement: [Sylvia Plachy, employer, Granta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Granta
Context triple: [Sylvia Plachy, employer, Granta]
  • A. Granta chosen
    Granta is a renowned British literary magazine known for publishing high-quality fiction, nonfiction, and reportage by established and emerging writers.
  • B. Granta Books
    Granta Books is a British independent publishing house known for literary fiction and narrative non-fiction, often associated with innovative and international writing.
  • C. The Paris Review
    The Paris Review is a renowned American literary magazine celebrated for publishing influential fiction, poetry, and in-depth writer interviews since the 1950s.
  • D. The English Review
    The English Review was an influential early 20th-century British literary magazine known for publishing innovative modernist writers under the editorship of Ford Madox Ford.
  • E. The New Yorker
    The New Yorker is an American magazine renowned for its in-depth journalism, literary fiction, cultural commentary, and distinctive cartoons.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d886d6ba8c819093215917b3d01689 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42db014b08190b88a5001e9f7811b completed April 19, 2026, 1:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a015fdc13d88190bbf9e6d1272814d2 completed May 11, 2026, 4:49 a.m.
Created at: April 10, 2026, 5:38 a.m.