Triple

T630044
Position Surface form Disambiguated ID Type / Status
Subject Margaret Craig McNamara E15905 entity
Predicate givenName P17 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 Craig McNamara, givenName, Margaret]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margaret
Context triple: [Margaret Craig McNamara, givenName, 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 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.
  • C. Eleanor
    Eleanor is a feminine given name most famously borne by Eleanor Roosevelt, the influential First Lady of the United States and human rights advocate.
  • D. Eleanor
    Eleanor was one of the merchant ships in Boston Harbor whose tea cargo was destroyed during the Boston Tea Party protest against British taxation in 1773.
  • E. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49ec051bc8190b3e3f8651a367d77 completed March 1, 2026, 8:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7cf498af4819085d494f85adf0825 completed March 4, 2026, 6:20 a.m.
Created at: March 1, 2026, 7:35 p.m.