Triple

T12146839
Position Surface form Disambiguated ID Type / Status
Subject Laura Harris E289346 entity
Predicate appearedIn P795 FINISHED
Object Dead Like Me E817381 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: Dead Like Me | Statement: [Laura Harris, appearedIn, Dead Like Me]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dead Like Me
Context triple: [Laura Harris, appearedIn, Dead Like Me]
  • A. Dead Like Me chosen
    Dead Like Me is a dark comedy-drama television series that follows a young woman who becomes a grim reaper after her sudden death, exploring themes of mortality and the afterlife with sardonic humor.
  • B. Dead Like You
    Dead Like You is a crime thriller novel by British author Peter James, featuring Detective Superintendent Roy Grace investigating a series of brutal attacks linked to a past case.
  • C. Playdead
    Playdead is a Danish independent video game developer best known for creating the critically acclaimed puzzle-platformers Limbo and Inside.
  • D. Dead Meat
    Dead Meat is a segment or component of the series "The Science of Things," likely focusing on scientific or educational content related to meat, decay, or biological processes.
  • E. Better Off Dead
    Better Off Dead is a thriller novel in the Jack Reacher series by Lee Child (co-written with Andrew Child), featuring the iconic drifter ex-military policeman embroiled in a dangerous conspiracy on the U.S.–Mexico border.
  • 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_69d6ab4c6710819097a9d228382dde43 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915ac2ebc81909155f9b2fb4a2252 completed April 10, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a7e24ac819083e85fb8edb2ed2c completed May 2, 2026, 2:30 p.m.
Created at: April 8, 2026, 9:49 p.m.