Triple

T11725223
Position Surface form Disambiguated ID Type / Status
Subject Monika Jaruzelska E278746 entity
Predicate givenName P17 FINISHED
Object Monika
Monika is a feminine given name commonly used in various European countries and beyond.
 E942997 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: Monika | Statement: [Monika Jaruzelska, givenName, Monika]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Monika
Context triple: [Monika Jaruzelska, givenName, Monika]
  • A. Monique
    Monique is the given name of the American comedian and Academy Award–winning actress Mo'Nique.
  • B. Mónica
    Mónica is the given name of Spanish singer and songwriter Mónica Naranjo, known for her powerful voice and dramatic pop music style.
  • C. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • D. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • E. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • 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: Monika
Triple: [Monika Jaruzelska, givenName, Monika]
Generated description
Monika is a feminine given name commonly used in various European countries and beyond.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Monika
Target entity description: Monika is a feminine given name commonly used in various European countries and beyond.
  • A. Monique
    Monique is the given name of the American comedian and Academy Award–winning actress Mo'Nique.
  • B. Mónica
    Mónica is the given name of Spanish singer and songwriter Mónica Naranjo, known for her powerful voice and dramatic pop music style.
  • C. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • D. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • E. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • 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_69d6aaffec6881908bead509e8621742 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4d603cc8190b2e68d0bdd793362 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef83d9fe70819089b9f3585188f96c completed April 27, 2026, 3:42 p.m.
NEDg Description generation batch_69ef96b13be881908102ffa867f96c22 completed April 27, 2026, 5:02 p.m.
NED2 Entity disambiguation (via description) batch_69efb51113708190998b570c33b9d0e7 completed April 27, 2026, 7:12 p.m.
Created at: April 8, 2026, 9:41 p.m.