Triple

T12841195
Position Surface form Disambiguated ID Type / Status
Subject Donna Hanover E307054 entity
Predicate employer P7 FINISHED
Object radio station WOR E817954 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: radio station WOR | Statement: [Donna Hanover, employer, radio station WOR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: radio station WOR
Context triple: [Donna Hanover, employer, radio station WOR]
  • A. WOR (AM) chosen
    WOR (AM) is a long-running New York City talk radio station known for its news, talk, and syndicated programming.
  • B. WRKO
    WRKO is a Boston-based AM radio station best known for its long-running news/talk format and role as one of New England’s major talk radio outlets.
  • C. WEEI (AM)
    WEEI (AM) is a Boston-based sports radio station known for its local and national sports talk programming and coverage of New England teams.
  • D. WMCA (New York) radio
    WMCA (New York) radio is a New York City AM radio station historically known for its influential sports coverage and popular music programming, including its famed role in broadcasting iconic baseball moments.
  • E. WWOR-TV
    WWOR-TV is a New York–area television station, historically known as a major independent and later network-affiliated outlet serving the New York City metropolitan market.
  • 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_69d7bdf52b94819096d6f0ba4ab50a98 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96ff2ab60819085561a3120189985 completed April 10, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f68edeaa1881909e06600ef88727de completed May 2, 2026, 11:55 p.m.
Created at: April 9, 2026, 5:35 p.m.