Triple

T7212636
Position Surface form Disambiguated ID Type / Status
Subject Patty Fenn E149443 entity
Predicate hasRelationshipWith P2830 FINISHED
Object Lee Gates (professional) E154560 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: Lee Gates (professional) | Statement: [Patty Fenn, hasRelationshipWith, Lee Gates (professional)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lee Gates (professional)
Context triple: [Patty Fenn, hasRelationshipWith, Lee Gates (professional)]
  • A. Lee Gates chosen
    Lee Gates is the brash, fast-talking financial TV host portrayed by George Clooney in the thriller film "Money Monster."
  • B. David Leech
    David Leech was an early settler and prominent local figure after whom the borough of Leechburg, Pennsylvania, was named.
  • C. Paul Lee
    Paul Lee is a British television executive and producer known for leading major networks such as ABC and for overseeing acclaimed series including "Mare of Easttown."
  • D. Bill Lee
    Bill Lee is an American jazz bassist and composer best known for his film scores, particularly for several of his son Spike Lee’s early movies.
  • E. Bill Lee
    Bill Lee was an American playback singer best known for providing the singing voices for numerous characters in classic Disney films.
  • 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_69c687eca814819095abb52316b1af80 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e98b61448190add3624a818fdc7b completed March 27, 2026, 8:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7bfcec3448190ab29c3742aa167ae completed March 28, 2026, 11:47 a.m.
Created at: March 27, 2026, 2:53 p.m.