Triple

T2658292
Position Surface form Disambiguated ID Type / Status
Subject Blood Diamond E54665 entity
Predicate mainCharacter P1183 FINISHED
Object Maddy Bowen E282397 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: Maddy Bowen | Statement: [Blood Diamond, mainCharacter, Maddy Bowen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maddy Bowen
Context triple: [Blood Diamond, mainCharacter, Maddy Bowen]
  • A. Maddy Bowen chosen
    Maddy Bowen is a determined American journalist in the film "Blood Diamond" who investigates the illicit diamond trade in war-torn Sierra Leone.
  • B. Mia Dolan
    Mia Dolan is an aspiring actress in Los Angeles and one of the two central protagonists of the musical film "La La Land."
  • C. Madeline Neroni
    Madeline Neroni is a captivating, manipulative, and physically disabled beauty in Anthony Trollope’s novel "Barchester Towers," known for using her charm and wit to influence the social and romantic intrigues around her.
  • D. Hadley Beeman
    Hadley Beeman is a web standards and technology governance expert known for her leadership within the World Wide Web Consortium (W3C) and related digital policy initiatives.
  • E. Mackenzie Mauzy
    Mackenzie Mauzy is an American actress and singer known for her work on Broadway and in film and television, including her role as Rapunzel in the 2014 musical fantasy film "Into the Woods."
  • 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_69ab49e028948190b97e01d73548b1d9 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd94dcaa48190aec625f68ce61a02 completed March 7, 2026, 7:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc02acc048190812d7d2d8b59058a completed March 10, 2026, 6:54 a.m.
Created at: March 6, 2026, 9:53 p.m.