Triple

T383227
Position Surface form Disambiguated ID Type / Status
Subject Dora Sigerson Shorter E8724 entity
Predicate hasGivenName P17 FINISHED
Object Dora
Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
 E49540 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: Dora | Statement: [Dora Sigerson Shorter, hasGivenName, Dora]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dora
Context triple: [Dora Sigerson Shorter, hasGivenName, Dora]
  • A. Hilda
    Hilda is the middle name of Margaret Thatcher, the former Prime Minister of the United Kingdom.
  • B. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • C. Nell
    Nell is a feminine given name, often used as a diminutive of names like Eleanor or Helen.
  • D. Niña
    Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
  • E. Sophia
    Sophia of the Palatinate was a 17th-century German princess and Electress of Hanover, best known as the mother of King George I of Great Britain and a key figure in the Protestant succession to the British throne.
  • 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: Dora
Triple: [Dora Sigerson Shorter, hasGivenName, Dora]
Generated description
Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dora
Target entity description: Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • A. Hilda
    Hilda is the middle name of Margaret Thatcher, the former Prime Minister of the United Kingdom.
  • B. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • C. Nell
    Nell is a feminine given name, often used as a diminutive of names like Eleanor or Helen.
  • D. Niña
    Niña was one of the three ships in Christopher Columbus’s 1492 voyage across the Atlantic, notable for its role in the first European expedition to the Americas.
  • E. Sophia
    Sophia of the Palatinate was a 17th-century German princess and Electress of Hanover, best known as the mother of King George I of Great Britain and a key figure in the Protestant succession to the British throne.
  • 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_69a2e7f47dd08190a4e294ccbbe46cd4 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ec40ff8c81909306eb2dfe1512af completed Feb. 28, 2026, 1:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4034e9fc88190af3bbd460019c025 completed March 1, 2026, 9:13 a.m.
NEDg Description generation batch_69a404d4bca08190ab26445f233d5d82 completed March 1, 2026, 9:20 a.m.
NED2 Entity disambiguation (via description) batch_69a4055b48fc81908a2b7fa1725bbdc4 completed March 1, 2026, 9:22 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.