Triple

T11378902
Position Surface form Disambiguated ID Type / Status
Subject Sportsnet World E269539 entity
Predicate network P2637 FINISHED
Object Sportsnet E54584 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: Sportsnet | Statement: [Sportsnet World, network, Sportsnet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sportsnet
Context triple: [Sportsnet World, network, Sportsnet]
  • A. Sportsnet chosen
    Sportsnet is a Canadian sports television network owned by Rogers Sports & Media that broadcasts a wide range of live sports, including Major League Baseball, NHL hockey, and other national and regional events.
  • B. Sportsnet World
    Sportsnet World is a Canadian specialty television channel focused on international soccer, rugby, and other global sports programming.
  • C. Sportsnet 590 The FAN
    Sportsnet 590 The FAN is a Toronto-based all-sports radio station known for its comprehensive coverage of local teams and major sporting events.
  • D. Réseau des sports
    Réseau des sports is a Canadian French-language specialty television channel focused on broadcasting sports events and related programming.
  • E. MSG Sportsnet
    MSG Sportsnet is a regional sports television channel in the New York metropolitan area that broadcasts live games and related programming for local professional and collegiate teams.
  • 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_69d6aacca1048190b39dbbc2174616fa completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7fc30f5d48190bb273df4c9e583a9 completed April 9, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5d32db0d081908f5a8f6ca1357997 completed April 20, 2026, 7:18 a.m.
Created at: April 8, 2026, 9:33 p.m.