Triple

T972586
Position Surface form Disambiguated ID Type / Status
Subject Martha Nussbaum E20976 entity
Predicate givenName P17 FINISHED
Object Martha E49016 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: Martha | Statement: [Martha Nussbaum, givenName, Martha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Martha
Context triple: [Martha Nussbaum, givenName, Martha]
  • A. Martha chosen
    Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
  • B. Martha Black
    Martha Black was a pioneering Canadian politician and naturalist, known as one of the first women elected to the Canadian Parliament and for her influential role in Yukon public life.
  • 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. Abigail
    Abigail is a feminine given name of Hebrew origin meaning "my father is joy," historically popular in English-speaking countries.
  • E. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • 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_69a493b33d2c81909c52c369d3ca8436 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b44c38f08190997e141d424e9e04 completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac2586fd7c8190ba77b327bad4bb69 completed March 7, 2026, 1:17 p.m.
Created at: March 1, 2026, 7:40 p.m.