Triple

T21656050
Position Surface form Disambiguated ID Type / Status
Subject Margaret Albu E534468 entity
Predicate hasGivenName P17 FINISHED
Object Margaret NE NERFINISHED

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 Albu, hasGivenName, Margaret]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margaret
Context triple: [Margaret Albu, hasGivenName, 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
    Margaret is a central character in the Australian television drama series "The Newsreader," which follows the turbulent personal and professional lives of broadcast journalists in the 1980s.
  • D. Margaret
    Margaret is a character who appears in the animated television series "Exit 9B" from the show "Regular Show."
  • E. Margaret
    Margaret is a fictional character from the DC Comics universe, associated with the supervillain team known as the Rogues.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c467e1f48190af2650b19175abc4 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef59185b208190a2cf4b8f54a2c231 completed April 27, 2026, 12:39 p.m.
Created at: April 16, 2026, 6:36 p.m.