Triple

T22535638
Position Surface form Disambiguated ID Type / Status
Subject The Understanding E557145 entity
Predicate producer P490 FINISHED
Object DJ Twinz NE NERFINISHED

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: DJ Twinz | Statement: [The Understanding, producer, DJ Twinz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: DJ Twinz
Context triple: [The Understanding, producer, DJ Twinz]
  • A. DJ Twinz chosen
    DJ Twinz is a highly acclaimed music producer and DJ recognized for influential, top-tier work in their genre.
  • B. DJ Jazzy Jeff
    DJ Jazzy Jeff is an American DJ, record producer, and turntablist best known for his pioneering work in hip hop and his partnership with Will Smith as DJ Jazzy Jeff & The Fresh Prince.
  • C. DJ Muggs
    DJ Muggs is an American DJ and producer best known as the primary producer for Cypress Hill and for his influential work in hip hop and trip-hop.
  • D. Lord Finesse
    Lord Finesse is an American hip-hop MC and producer from the Bronx, best known as a founding member of the Diggin' in the Crates Crew and for his influential work in 1990s East Coast rap.
  • E. DJ Akademiks
    DJ Akademiks is a Jamaican-American media personality and hip-hop commentator known for his YouTube coverage of rap news, online beefs, and cultural controversies.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e57483c8190b0887c4f8ff26446 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15edad0248190b990ddffbc786e05 completed April 29, 2026, 1:28 a.m.
Created at: April 16, 2026, 8:51 p.m.