Triple

T7230176
Position Surface form Disambiguated ID Type / Status
Subject She's Gotta Have It E154880 entity
Predicate televisionAdaptationPlatform P48296 FINISHED
Object Netflix E118902 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: Netflix | Statement: [She's Gotta Have It, televisionAdaptationPlatform, Netflix]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Netflix
Context triple: [She's Gotta Have It, televisionAdaptationPlatform, Netflix]
  • A. Netflix chosen
    Netflix is a global streaming entertainment company best known for its vast library of films and TV series and its influential original content.
  • B. Hulu
    Hulu is a U.S.-based subscription streaming service offering on-demand access to a wide range of television shows, films, and original content.
  • C. Amazon Prime Video
    Amazon Prime Video is a subscription-based streaming service from Amazon that offers a wide range of movies, TV series, and original content available on-demand across multiple devices.
  • D. HBO Max
    HBO Max is a streaming service from WarnerMedia that offers a wide library of movies, series, and original content from HBO and related brands.
  • E. Amazon Freevee
    Amazon Freevee is a free, ad-supported streaming service from Amazon that offers a mix of movies, TV shows, and original programming.
  • 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_69c68811dd1c8190ac460bb39e64e1f0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ea0d9b6c8190a0b5f0ab8d5cca19 completed March 27, 2026, 8:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cbfb46388190992cc98039e71748 completed March 28, 2026, 12:39 p.m.
Created at: March 27, 2026, 2:54 p.m.