Triple

T2220003
Position Surface form Disambiguated ID Type / Status
Subject B'Day E48118 entity
Predicate producer P490 FINISHED
Object Sean Garrett E231598 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: Sean Garrett | Statement: [B'Day, producer, Sean Garrett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sean Garrett
Context triple: [B'Day, producer, Sean Garrett]
  • A. Sean Garrett chosen
    Sean Garrett is an American songwriter and record producer known for crafting numerous chart-topping R&B and hip-hop hits for major artists in the 2000s and 2010s.
  • B. Seth Gabel
    Seth Gabel is an American actor known for his roles in television series such as "Fringe," "Salem," and "Nip/Tuck."
  • C. Corey Stoll
    Corey Stoll is an American actor known for his prominent roles in film and television, including his acclaimed performance as Congressman Peter Russo in the political drama series "House of Cards."
  • D. Logan Marshall-Green
    Logan Marshall-Green is an American actor and director known for his roles in films like "Prometheus" and "Upgrade" as well as various television series.
  • E. Jonathan Tucker
    Jonathan Tucker is an American actor known for his intense, character-driven roles in film and television, including prominent performances in series like "Kingdom," "Westworld," and "City on a Hill."
  • 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_69a88aa1ee708190862c8c378c41e9eb completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc01386588190a9507f2969a201ca completed March 7, 2026, 6:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf1cded88190aa8edefc5dd94a6c completed March 9, 2026, 12:37 p.m.
Created at: March 4, 2026, 7:46 p.m.