Triple

T11447263
Position Surface form Disambiguated ID Type / Status
Subject Pieces of a Woman E271296 entity
Predicate starring P1507 FINISHED
Object Molly Parker E138123 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: Molly Parker | Statement: [Pieces of a Woman, starring, Molly Parker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Molly Parker
Context triple: [Pieces of a Woman, starring, Molly Parker]
  • A. Molly Parker chosen
    Molly Parker is a Canadian actress known for her nuanced performances in film and television, including prominent roles in series such as House of Cards and Deadwood.
  • B. Betty Gilpin
    Betty Gilpin is an American actress best known for her Emmy-nominated role in the Netflix series "GLOW" and performances in films such as "The Hunt" and "The Tomorrow War."
  • C. Alice Patten
    Alice Patten is a British actress best known internationally for her role as an English documentary filmmaker in the acclaimed Indian film "Rang De Basanti."
  • D. Jessica Hynes
    Jessica Hynes is an English actress, writer, and comedian best known for co-creating and starring in the sitcom "Spaced" and for her roles in British television and film.
  • E. Melissa Hudson
    Melissa Hudson is known as the daughter of Stanley Hudson, a character from the American television series "The Office."
  • 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_69d6aadff8888190a13f253f0d460874 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d81c6d4890819082fb4a670feb2629 completed April 9, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69e6e7800ca881909c1816a74b3b8f19 completed April 21, 2026, 2:57 a.m.
Created at: April 8, 2026, 9:35 p.m.