Triple

T2723559
Position Surface form Disambiguated ID Type / Status
Subject Maria Louisa Garland E60136 entity
Predicate givenName P17 FINISHED
Object Louisa E164603 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: Louisa | Statement: [Maria Louisa Garland, givenName, Louisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Louisa
Context triple: [Maria Louisa Garland, givenName, Louisa]
  • A. Louisa chosen
    Louisa is the middle name of Katharine Louisa Stanley, a 19th-century English writer and member of the prominent Stanley family.
  • B. Louisa
    Louisa is a fictional character from Jean Toomer’s modernist work "Cane," representing themes of love, memory, and the complexities of African American life in the early 20th-century South.
  • C. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • D. Margaret
    Margaret is a 2011 American drama film written and directed by Kenneth Lonergan, known for its complex portrayal of grief and moral responsibility following a tragic bus accident in New York City.
  • E. Eliza
    Eliza is a feminine given name of Hebrew origin, often considered a shortened form of Elizabeth and commonly used in English-speaking countries.
  • 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_69ab4b746d248190958e052045c09255 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdaca581881908fe8d3d820f839b7 completed March 7, 2026, 7:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc033d94481908e4709529ab93442 completed March 10, 2026, 6:54 a.m.
Created at: March 6, 2026, 9:55 p.m.