Triple

T10845357
Position Surface form Disambiguated ID Type / Status
Subject In Her Skin E255995 entity
Predicate hasCastMember P2308 FINISHED
Object Ruth Bradley E949502 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: Ruth Bradley | Statement: [In Her Skin, hasCastMember, Ruth Bradley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ruth Bradley
Context triple: [In Her Skin, hasCastMember, Ruth Bradley]
  • A. Ruth Bradley chosen
    Ruth Bradley is an Irish actress known for her roles in television series such as "Humans" and "Primeval," as well as various film and stage productions.
  • B. Ruth Storey
    Ruth Storey was an American actress active in the mid-20th century, known for her supporting roles in film and television.
  • C. Helene Bradley
    Helene Bradley is a fictional character appearing in Ernest Hemingway’s novel "To Have and Have Not."
  • D. Ruth McCord
    Ruth McCord is known primarily as the wife of James W. McCord Jr., a former CIA officer and key figure in the Watergate scandal.
  • E. Ruth Jamison
    Ruth Jamison is a central, compassionate character in the novel and film "Fried Green Tomatoes," known for her deep friendship with Idgie Threadgoode and her role in the story’s themes of love, resilience, and female solidarity.
  • 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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d750d0155c81908fb55ba6b45db800 completed April 9, 2026, 7:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69f470cbce14819099d47d468ae61df7 completed May 1, 2026, 9:22 a.m.
Created at: April 8, 2026, 9:19 p.m.