Triple

T14610365
Position Surface form Disambiguated ID Type / Status
Subject Buzz Bissinger E342942 entity
Predicate employer P7 FINISHED
Object The Daily Beast E455756 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: The Daily Beast | Statement: [Buzz Bissinger, employer, The Daily Beast]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Daily Beast
Context triple: [Buzz Bissinger, employer, The Daily Beast]
  • A. The Daily Beast chosen
    The Daily Beast is an American news and opinion website known for its sharp political commentary, investigative reporting, and pop culture coverage.
  • B. The Daily Caller
    The Daily Caller is a conservative American news and opinion website known for its right-leaning political coverage and commentary.
  • C. Huffington
    Huffington is the surname of Arianna Huffington, the Greek-American author, media entrepreneur, and co-founder of The Huffington Post.
  • D. The Daily
    The Daily is Statistics Canada’s official online publication that provides timely statistical news, analysis, and key data releases about Canada’s economy, society, and environment.
  • E. The New York Observer
    The New York Observer is a New York City–based weekly newspaper and online publication known for its coverage of local politics, media, culture, and real estate.
  • 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_69d822dec68081908c2553145c4051dc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb44f0dd48190a78662b5998a6722 completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fda91f437c8190ada4d1c3708faedd completed May 8, 2026, 9:13 a.m.
Created at: April 10, 2026, 1:25 a.m.