Triple

T274157
Position Surface form Disambiguated ID Type / Status
Subject Black E5209 entity
Predicate hasNotableBearer P458 FINISHED
Object 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.
 E35246 NE FINISHED

How this triple was built (4 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 Black | Statement: [Black, hasNotableBearer, Martha Black]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martha Black
Context triple: [Black, hasNotableBearer, Martha Black]
  • A. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • B. Bess
    Bess was the familiar nickname of Elizabeth "Bess" Truman, the First Lady of the United States and wife of President Harry S. Truman.
  • C. Bathsheba
    Bathsheba is a prominent biblical figure known as the wife of King David and the mother of King Solomon.
  • D. Tessie
    Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
  • E. Abigail
    Abigail is a feminine given name of Hebrew origin meaning "my father is joy," historically popular in English-speaking countries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Martha Black
Triple: [Black, hasNotableBearer, Martha Black]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martha Black
Target entity description: 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.
  • A. Margaret
    Margaret is a feminine given name of Greek origin, traditionally associated with the meaning "pearl" and widely used in English-speaking countries.
  • B. Bess
    Bess was the familiar nickname of Elizabeth "Bess" Truman, the First Lady of the United States and wife of President Harry S. Truman.
  • C. Bathsheba
    Bathsheba is a prominent biblical figure known as the wife of King David and the mother of King Solomon.
  • D. Tessie
    Tessie is a Boston Red Sox mascot character, often depicted as a green monster and associated with Wally the Green Monster.
  • E. Abigail
    Abigail is a feminine given name of Hebrew origin meaning "my father is joy," historically popular in English-speaking countries.
  • F. None of above. chosen

Provenance (5 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_69a257e6c8788190987dfe705ca2912a completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25dd0a99c819089968a5400c58c5f completed Feb. 28, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a38f5471d88190bcb8b9117575555b completed March 1, 2026, 12:59 a.m.
NEDg Description generation batch_69a38fe412e08190990e7bff3d74d9da completed March 1, 2026, 1:01 a.m.
NED2 Entity disambiguation (via description) batch_69a3903ee4688190a12a33ae1029e9de completed March 1, 2026, 1:02 a.m.
Created at: Feb. 28, 2026, 2:59 a.m.