Triple

T6832670
Position Surface form Disambiguated ID Type / Status
Subject Cooke Maroney E157174 entity
Predicate mediaCoverageFocus P72922 FINISHED
Object relationship with Jennifer Lawrence LITERAL 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: relationship with Jennifer Lawrence | Statement: [Cooke Maroney, mediaCoverageFocus, relationship with Jennifer Lawrence]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: mediaCoverageFocus
Context triple: [Cooke Maroney, mediaCoverageFocus, relationship with Jennifer Lawrence]
  • A. mediaCoverageAs
    Indicates that one entity provides or receives media coverage in the role, capacity, or format specified by another entity.
  • B. mediaCoverage
    Indicates that one entity reports on, documents, or broadcasts information about another entity through news or media channels.
  • C. mediaCoverageControversy
    Indicates that media coverage is associated with, contributes to, or centers around a controversy involving the related entities.
  • D. mediaCovered
    Indicates that one entity (such as a media outlet or source) has reported on, featured, or otherwise provided coverage of another entity or event.
  • E. mediaCoverageReason
    Indicates the reason or justification for which a particular subject, event, or entity is being covered or reported on by the media.
  • F. None of above. chosen

Provenance (4 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_69c6882a5b5c8190917a7db9ed36bad1 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d62b1e8c8190a81d91191a54b073 completed March 27, 2026, 7:10 p.m.
PD Predicate disambiguation batch_69c6d09d95f0819091ca7f897dc21efe completed March 27, 2026, 6:46 p.m.
PDg Predicate description generation batch_69c6d11fab808190b18160ff3829fcc6 completed March 27, 2026, 6:49 p.m.
Created at: March 27, 2026, 2:18 p.m.