Triple

T10515666
Position Surface form Disambiguated ID Type / Status
Subject Dora Sigerson Shorter E248023 entity
Predicate hasGivenName P17 FINISHED
Object Dora E49540 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: 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. Dora chosen
    Dora is the given name of Dora Sigerson Shorter, an Irish poet associated with the late 19th- and early 20th-century literary revival.
  • B. Dora
    Dora is a character in Jim Jarmusch’s film "Broken Flowers," known as one of Don Johnston’s former girlfriends whom he visits while searching for the mother of his alleged son.
  • C. Dora
    Dora is the given name of Dora de Houghton Carrington, an English painter and decorative artist associated with the Bloomsbury Group.
  • D. Dora Riparia
    Dora Riparia is a river in northwestern Italy that flows through the city of Turin before joining the Po River.
  • E. Dora the Explorer
    Dora the Explorer is a popular animated children's television series featuring a young Latina girl who embarks on interactive adventures while teaching viewers basic problem-solving and Spanish vocabulary.
  • 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_69d381c4aa948190942e1d803143fb0e completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d509cbacb08190a446c864b97823ad completed April 7, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d9882acd348190ae3ab2f17c834aef completed April 10, 2026, 11:30 p.m.
Created at: April 6, 2026, 12:28 p.m.