Triple

T7951400
Position Surface form Disambiguated ID Type / Status
Subject 1000 Ways to Die E184621 entity
Predicate executiveProducer P7225 FINISHED
Object Thom Beers E708295 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: Thom Beers | Statement: [1000 Ways to Die, executiveProducer, Thom Beers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thom Beers
Context triple: [1000 Ways to Die, executiveProducer, Thom Beers]
  • A. Thom Beers chosen
    Thom Beers is an American television producer and narrator best known for creating and producing gritty, reality-based series such as "Deadliest Catch" and other shows focused on dangerous occupations and extreme situations.
  • B. Mike Beedle
    Mike Beedle was a software engineer, author, and early proponent of agile and Scrum methodologies who helped popularize agile software development practices worldwide.
  • C. Harry Bradbeer
    Harry Bradbeer is a British television and film director best known for his work on acclaimed series like "Fleabag" and the mystery film "Enola Holmes."
  • D. Andy Fickman
    Andy Fickman is an American film and television director known for family-friendly comedies and energetic, commercially successful studio movies.
  • E. Michael Krieger
    Michael Krieger is a fictional character appearing in the story of "Watch Over Me."
  • 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_69ca8292cba881908a64427b938dac47 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b5b7450819091e4e6f21e9d832d completed March 31, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63af95fc81908608dcb0eb7e9893 completed April 1, 2026, 12:15 a.m.
Created at: March 30, 2026, 5:10 p.m.