Triple

T14694596
Position Surface form Disambiguated ID Type / Status
Subject David Ebershoff E345120 entity
Predicate employer P7 FINISHED
Object Random House E8970 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: Random House | Statement: [David Ebershoff, employer, Random House]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Random House
Context triple: [David Ebershoff, employer, Random House]
  • A. Random House chosen
    Random House is a major American book publishing company known for releasing a wide range of influential fiction and nonfiction titles.
  • B. Penguin Random House
    Penguin Random House is a major global trade book publisher known for its extensive catalog of fiction and nonfiction titles across numerous imprints.
  • C. Random House Studio
    Random House Studio is a media production division associated with the major publishing company Random House, focused on developing and adapting content for film, television, and other entertainment platforms.
  • D. Simon & Schuster
    Simon & Schuster is a major American publishing company known for producing a wide range of bestselling fiction and nonfiction books.
  • E. Doubleday
    Doubleday is a major American publishing company known for releasing a wide range of influential fiction and nonfiction books.
  • 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_69d822e34b348190ada4d1cdb6c7c226 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb586e7108190be644db9cf9a4d99 completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdf08274ac8190b5ba0752d36a690b completed May 8, 2026, 2:17 p.m.
Created at: April 10, 2026, 1:28 a.m.