Triple

T9876818
Position Surface form Disambiguated ID Type / Status
Subject Markéta Vaňková E240091 entity
Predicate givenName P17 FINISHED
Object Markéta
Markéta is a common Czech female given name, equivalent to Margaret in English.
 E826528 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: Markéta | Statement: [Markéta Vaňková, givenName, Markéta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Markéta
Context triple: [Markéta Vaňková, givenName, Markéta]
  • A. Martina
    Martina was a Byzantine empress and the second wife of Emperor Heraclius, known for her controversial influence at court and her role in the empire’s turbulent 7th-century politics.
  • B. Martina
    Martina is a feminine given name of Latin origin, commonly used in many European and Spanish-speaking countries.
  • C. Arleta
    Arleta is a residential neighborhood in the San Fernando Valley region of Los Angeles, California.
  • D. Haná
    Haná is a historical ethnographic region in central Moravia in the Czech Republic, known for its fertile agricultural land, distinctive folk traditions, and Hanakian dialect.
  • E. Libuše
    Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
  • 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: Markéta
Triple: [Markéta Vaňková, givenName, Markéta]
Generated description
Markéta is a common Czech female given name, equivalent to Margaret in English.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Markéta
Target entity description: Markéta is a common Czech female given name, equivalent to Margaret in English.
  • A. Martina
    Martina was a Byzantine empress and the second wife of Emperor Heraclius, known for her controversial influence at court and her role in the empire’s turbulent 7th-century politics.
  • B. Martina
    Martina is a feminine given name of Latin origin, commonly used in many European and Spanish-speaking countries.
  • C. Arleta
    Arleta is a residential neighborhood in the San Fernando Valley region of Los Angeles, California.
  • D. Haná
    Haná is a historical ethnographic region in central Moravia in the Czech Republic, known for its fertile agricultural land, distinctive folk traditions, and Hanakian dialect.
  • E. Libuše
    Libuše is a Czech opera by Bedřich Smetana, centered on the legendary princess Libuše who prophesies the glory of Prague and the Czech nation.
  • 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_69ca84e8a0788190b9061811d50fd554 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3fb58d481908407898912c4b4e9 completed April 2, 2026, 12:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1e47b62388190a033743376500375 completed April 5, 2026, 4:26 a.m.
NEDg Description generation batch_69d1e5d0da7081908e14fe4bc6623ea5 completed April 5, 2026, 4:32 a.m.
NED2 Entity disambiguation (via description) batch_69d1e6af89f88190abe63f8172182f58 completed April 5, 2026, 4:35 a.m.
Created at: March 30, 2026, 8:37 p.m.