Triple

T7276585
Position Surface form Disambiguated ID Type / Status
Subject Dominika Egorova E163044 entity
Predicate givenName P17 FINISHED
Object Dominika
Dominika is a feminine given name of Slavic origin, commonly used in countries such as Poland, the Czech Republic, and Slovakia.
E653803 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: Dominika | Statement: [Dominika Egorova, givenName, Dominika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dominika
Context triple: [Dominika Egorova, givenName, Dominika]
  • A. Leona Vicario
    Leona Vicario was a prominent Mexican independence heroine, journalist, and supporter of the insurgent cause against Spanish rule in the early 19th century.
  • B. Donna
    Donna is a feminine given name of Italian origin that has been widely used in English-speaking countries.
  • C. Claricia Scotti
    Claricia Scotti was a medieval Italian noblewoman known primarily as the mother of Pope Innocent III.
  • D. Adrienne
    Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
  • E. Claretta
    Claretta was the nickname of Claretta Petacci, the Italian mistress of dictator Benito Mussolini who was executed alongside him in 1945.
  • 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: Dominika
Triple: [Dominika Egorova, givenName, Dominika]
Generated description
Dominika is a feminine given name of Slavic origin, commonly used in countries such as Poland, the Czech Republic, and Slovakia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Dominika
Target entity description: Dominika is a feminine given name of Slavic origin, commonly used in countries such as Poland, the Czech Republic, and Slovakia.
  • A. Leona Vicario
    Leona Vicario was a prominent Mexican independence heroine, journalist, and supporter of the insurgent cause against Spanish rule in the early 19th century.
  • B. Donna
    Donna is a feminine given name of Italian origin that has been widely used in English-speaking countries.
  • C. Claricia Scotti
    Claricia Scotti was a medieval Italian noblewoman known primarily as the mother of Pope Innocent III.
  • D. Adrienne
    Adrienne is a feminine given name of French origin, commonly used in English- and French-speaking countries.
  • E. Claretta
    Claretta was the nickname of Claretta Petacci, the Italian mistress of dictator Benito Mussolini who was executed alongside him in 1945.
  • 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_69c6885c5964819085b209701769877f completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6eb2f239c819097c1ac4d6de8b0e5 completed March 27, 2026, 8:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7db3110688190bf52180ea159c91c completed March 28, 2026, 1:44 p.m.
NEDg Description generation batch_69c7dbf65fb08190ae8a9c4e57d42e97 completed March 28, 2026, 1:47 p.m.
NED2 Entity disambiguation (via description) batch_69c7dc6873a081908ea4e953430ec20b completed March 28, 2026, 1:49 p.m.
Created at: March 27, 2026, 2:59 p.m.