Triple

T16836245
Position Surface form Disambiguated ID Type / Status
Subject Sophia Fermor E409287 entity
Predicate givenName P17 FINISHED
Object Sophia E306547 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: Sophia | Statement: [Sophia Fermor, givenName, Sophia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sophia
Context triple: [Sophia Fermor, givenName, Sophia]
  • A. 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.
  • B. Sophia
    Sophia is a philosophical and theological concept signifying divine wisdom, often personified and associated with the rational principle of the cosmos.
  • C. Sophia chosen
    Sophia is a person whose given name is used in the full name Sophia Chew Nicklin Dallas.
  • D. Sophia
    Sophia is a small town located in Raleigh County in the southern part of West Virginia, United States.
  • E. Sophia
    Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
  • 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_69d883952b048190887740a980b712ed completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b34d49d08190aa62f05d67244584 completed April 18, 2026, 4:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb15c9f48190a4d73bb08a15beb4 completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:23 a.m.