Triple

T6126928
Position Surface form Disambiguated ID Type / Status
Subject Tulip Fever E136616 entity
Predicate mainCharacter P1183 FINISHED
Object Sophia
Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
E576541 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: Sophia | Statement: [Tulip Fever, mainCharacter, Sophia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sophia
Context triple: [Tulip Fever, mainCharacter, 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
    "Sophia" is a lesser-known literary work by British novelist Anthony Hope, best known for his adventure classic "The Prisoner of Zenda."
  • D. Sophia
    Sophia is a small town located in Raleigh County in the southern part of West Virginia, United States.
  • E. Sophia
    Sophia is a feminine given name of Greek origin meaning "wisdom," widely used across many cultures and languages.
  • 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: Sophia
Triple: [Tulip Fever, mainCharacter, Sophia]
Generated description
Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sophia
Target entity description: Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
  • A. Sophia
    Sophia is a person whose given name is used in the full name Sophia Chew Nicklin Dallas.
  • B. Sophia
    Sophia is the given name of Queen Sofía of Spain, the former queen consort known for her long-standing role in the Spanish royal family and public life.
  • C. Sophia
    Sophia was a prominent Byzantine empress of the Justinian dynasty, known for her political influence and role in imperial court affairs during the 6th century.
  • D. Sophia
    "Sophia" is a lesser-known literary work by British novelist Anthony Hope, best known for his adventure classic "The Prisoner of Zenda."
  • E. Sophia
    Sophia is a feminine given name of Greek origin meaning "wisdom," widely used across many cultures and languages.
  • 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_69c008a0a37c81908e5b4f879158afb3 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05c2a13a48190b80e11d58fc87c8a completed March 22, 2026, 9:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c16ec6b6648190801da4781246c27e completed March 23, 2026, 4:48 p.m.
NEDg Description generation batch_69c1df0bcabc819097560894ac10ca92 completed March 24, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69c1df750db481908a6281e02cd5f445 completed March 24, 2026, 12:48 a.m.
Created at: March 22, 2026, 4:15 p.m.