Triple

T10598338
Position Surface form Disambiguated ID Type / Status
Subject Doctor Dré E275675 entity
Predicate name P16 FINISHED
Object Doctor Dré E275675 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: Doctor Dré | Statement: [Doctor Dré, name, Doctor Dré]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Doctor Dré
Context triple: [Doctor Dré, name, Doctor Dré]
  • A. Doctor Dré chosen
    Doctor Dré is an American DJ and television personality best known for co-hosting the influential hip-hop music video show "Yo! MTV Raps" in the late 1980s and early 1990s.
  • B. Schoolly D
    Schoolly D is an American rapper and pioneer of gangsta rap from Philadelphia, known for his gritty, influential early recordings in the mid-1980s.
  • C. Kenny Dope
    Kenny Dope is an influential American DJ and producer best known as one half of the house music duo Masters at Work and for his genre-spanning work in house, hip-hop, and Latin-influenced dance music.
  • D. Canibus
    Canibus is an American rapper known for his complex, battle-oriented lyricism and technically intricate rhyme schemes.
  • E. Father MC
    Father MC is an American hip hop artist known for his early 1990s work that blended rap with R&B influences and helped showcase future stars like Mary J. Blige and Jodeci.
  • 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_69d6aaf948d88190806cc3a8c47a3fb2 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d6ded358248190ba9268a51b2805fc completed April 8, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69d95e9c1a7c8190a91ad479518e411f completed April 10, 2026, 8:33 p.m.
Created at: April 8, 2026, 7:28 p.m.