Triple

T9975674
Position Surface form Disambiguated ID Type / Status
Subject Margaret, Alabama E196320 entity
Predicate hasName P744 FINISHED
Object Margaret E17722 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: Margaret | Statement: [Margaret, Alabama, hasName, Margaret]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margaret
Context triple: [Margaret, Alabama, hasName, Margaret]
  • A. Margaret chosen
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • B. 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.
  • C. Margaret Mara
    Margaret Mara is a person notable enough to be recognized as a bearer of the surname Mara, though specific widely known public details about her are not clearly established.
  • D. Margaret Rose
    Margaret Rose, better known as Princess Margaret, was the younger sister of Queen Elizabeth II and a prominent British royal noted for her glamorous yet often controversial life.
  • E. Marjorie
    Marjorie is a feminine given name of French origin that has been widely 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_69ca82eea2b88190a0e511d21a31f386 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb84b47308190aa2f94fa7320cdc3 completed April 2, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d257c9f6cc81908256dc1e8d6c3fea completed April 5, 2026, 12:38 p.m.
Created at: March 30, 2026, 8:48 p.m.