Triple

T2129485
Position Surface form Disambiguated ID Type / Status
Subject Hart of Dixie E46503 entity
Predicate stars P1956 FINISHED
Object Rachel Bilson E243720 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: Rachel Bilson | Statement: [Hart of Dixie, stars, Rachel Bilson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rachel Bilson
Context triple: [Hart of Dixie, stars, Rachel Bilson]
  • A. Rachel Bilson chosen
    Rachel Bilson is an American actress best known for her television roles, including starring in the series "The O.C." and other popular TV dramas and comedies.
  • B. Laura Prepon
    Laura Prepon is an American actress best known for her roles on the television series That '70s Show and Orange Is the New Black.
  • C. Jennifer Love Hewitt
    Jennifer Love Hewitt is an American actress and singer best known for her roles in 1990s and 2000s film and television, including the horror and teen drama genres.
  • D. Jillian Bell
    Jillian Bell is an American comedian, actress, and writer known for her sharp, offbeat humor in films and TV shows such as "Workaholics," "22 Jump Street," and "Brittany Runs a Marathon."
  • E. Lacey Chabert
    Lacey Chabert is an American actress and voice actress best known for her roles in the film "Mean Girls," numerous Hallmark Channel movies, and early voice work in animated series.
  • 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_69a88a1626548190ae59a5028c3baa8e completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbb77ccc4819087bee5dbb91b5ae8 completed March 7, 2026, 5:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71ac8a0081909cbb1187513bfc56 completed March 9, 2026, 7:07 a.m.
Created at: March 4, 2026, 7:44 p.m.