Triple

T8472444
Position Surface form Disambiguated ID Type / Status
Subject Original Amiga chipset E200310 entity
Predicate mainComponent P14071 FINISHED
Object Denise E214650 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: Denise | Statement: [Original Amiga chipset, mainComponent, Denise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Denise
Context triple: [Original Amiga chipset, mainComponent, Denise]
  • A. Denise chosen
    Denise is the custom video display chip used in early Commodore Amiga computers, responsible for handling their advanced graphics and sprite capabilities.
  • B. Denise
    Denise is a central character in the comedy film "Hot Rod," serving as Rod Kimble's kind-hearted and supportive love interest.
  • C. Denise Robert
    Denise Robert is a Canadian film producer known for her work on acclaimed Quebec cinema, including collaborations with director Denys Arcand.
  • D. Diane
    Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
  • E. Danielle
    Danielle is the young prodigy and central superheroine of the novel "Chronicles of a Superheroine," known for using her intelligence and creativity to tackle global challenges.
  • 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_69ca831a4f348190bfdd09250e86ae35 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe4f4fbf481909e4fd7c078b27477 completed March 31, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce39fc9bf481908e37919b13465d18 completed April 2, 2026, 9:42 a.m.
Created at: March 30, 2026, 6:11 p.m.