Triple

T6305028
Position Surface form Disambiguated ID Type / Status
Subject Mildred Spiewak E141352 entity
Predicate nickname P55 FINISHED
Object Queen of Carbon E123265 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: Queen of Carbon | Statement: [Mildred Spiewak, nickname, Queen of Carbon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Queen of Carbon
Context triple: [Mildred Spiewak, nickname, Queen of Carbon]
  • A. Queen of Carbon chosen
    Queen of Carbon is the nickname of physicist Mildred Dresselhaus, renowned for her pioneering research on the electronic properties of carbon materials such as graphite, fullerenes, and carbon nanotubes.
  • B. All Hail the Queen
    All Hail the Queen is Queen Latifah’s influential 1989 debut hip-hop album that helped establish her as a pioneering female voice in rap and the Native Tongues movement.
  • C. Queen of Queens
    Queen of Queens is the exalted royal title borne by Tamar of Georgia, reflecting her status as a supreme and sovereign monarch in medieval Georgian history.
  • D. A Speck of Dust
    A Speck of Dust is a stand-up comedy special by Sarah Silverman featuring her characteristic blend of sharp social commentary and personal storytelling.
  • E. The Catalyst
    The Catalyst is a prominent innovation and business hub building at the University of York’s Heslington East campus, designed to support startups, enterprise, and collaborative research.
  • 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_69c008cf0ad4819095def81e2bd42f9f completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0645f26a881909d5746151c0843cc completed March 22, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e44527488190b3d605e917c8dfb2 completed March 27, 2026, 1:58 a.m.
Created at: March 22, 2026, 4:28 p.m.