Triple

T10810761
Position Surface form Disambiguated ID Type / Status
Subject Tea for Two E255092 entity
Predicate starring P1507 FINISHED
Object Bill Goodwin E338594 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: Bill Goodwin | Statement: [Tea for Two, starring, Bill Goodwin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bill Goodwin
Context triple: [Tea for Two, starring, Bill Goodwin]
  • A. Bill Goodwin chosen
    Bill Goodwin was an American radio and television announcer and actor best known for his work on comedy programs in the 1940s and 1950s.
  • B. Jerry Goodwin
    Jerry Goodwin is the individual for whom Goodwin Field, a baseball stadium, is named, indicating his significant contribution or connection to the facility or its associated program.
  • C. Jeremy Goodwin
    Jeremy Goodwin is a brilliant but socially awkward sports statistician and associate producer on the television series "Sports Night."
  • D. Brian Goodman
    Brian Goodman is an American actor and director known for his character roles in film and television, including a notable part in the crime drama series "Rizzoli & Isles."
  • E. Roger Goodman
    Roger Goodman is a television director and producer best known for directing major live broadcasts and award shows, including the Academy Awards.
  • 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d733b7bfac8190b6ae34144376d6ad completed April 9, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb0ea2b6481909dfd94fe0c3c4499 completed April 14, 2026, 9:26 p.m.
Created at: April 8, 2026, 9:18 p.m.