Triple

T14540194
Position Surface form Disambiguated ID Type / Status
Subject Hollywood (miniseries) E341146 entity
Predicate hasCastMember P2308 FINISHED
Object Laura Harrier E538156 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: Laura Harrier | Statement: [Hollywood (miniseries), hasCastMember, Laura Harrier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Harrier
Context triple: [Hollywood (miniseries), hasCastMember, Laura Harrier]
  • A. Laura Harrier chosen
    Laura Harrier is an American actress and model best known for her roles in films like "Spider-Man: Homecoming" and "BlacKkKlansman."
  • B. Stephanie Hsu
    Stephanie Hsu is an American actress best known for her acclaimed, genre-bending performance as Joy/Jobu Tupaki in the film "Everything Everywhere All at Once."
  • C. Brianne Tju
    Brianne Tju is an American actress known for her roles in teen and horror television series and films, including the thriller "47 Meters Down: Uncaged."
  • D. Brenda Song
    Brenda Song is an American actress known for her roles in Disney Channel productions like "The Suite Life of Zack & Cody" and in acclaimed films such as "The Social Network."
  • E. Jessica Soho
    Jessica Soho is a prominent Filipino broadcast journalist and television news anchor renowned for her in-depth documentaries and long-running public affairs programs.
  • 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_69d822dac79c8190a84a073f3cbaced5 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb1bd0dd4819094c8b2f2aa6b1c5e completed April 14, 2026, 9:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd7a5cca788190aa8762d860c78721 completed May 8, 2026, 5:53 a.m.
Created at: April 10, 2026, 1:22 a.m.