Triple

T2179939
Position Surface form Disambiguated ID Type / Status
Subject Martha E49016 entity
Predicate hasCognate P2525 FINISHED
Object Marta (Czech)
Marta is the Czech form of the female given name Martha, commonly used in Czech-speaking countries.
 E243816 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: Marta (Czech) | Statement: [Martha, hasCognate, Marta (Czech)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marta (Czech)
Context triple: [Martha, hasCognate, Marta (Czech)]
  • A. Marta (Polish)
    Marta is a common Polish female given name, equivalent to Martha, traditionally associated with Christian and European naming traditions.
  • B. Marta (Spanish)
    Marta is the Spanish given name equivalent to Martha, commonly used in Spanish-speaking countries.
  • C. Marta (Scandinavian languages)
    Marta is the Scandinavian form of the female given name Martha, commonly used in countries such as Sweden, Norway, and Denmark.
  • D. Terézia Mora
    Terézia Mora is a Hungarian-born German writer and translator acclaimed for her innovative prose and contributions to contemporary German-language literature.
  • E. Milena Králíčková
    Milena Králíčková is a Czech academic and physician who serves as the rector of Charles University in Prague.
  • 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: Marta (Czech)
Triple: [Martha, hasCognate, Marta (Czech)]
Generated description
Marta is the Czech form of the female given name Martha, commonly used in Czech-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marta (Czech)
Target entity description: Marta is the Czech form of the female given name Martha, commonly used in Czech-speaking countries.
  • A. Marta (Polish)
    Marta is a common Polish female given name, equivalent to Martha, traditionally associated with Christian and European naming traditions.
  • B. Marta (Spanish)
    Marta is the Spanish given name equivalent to Martha, commonly used in Spanish-speaking countries.
  • C. Marta (Scandinavian languages)
    Marta is the Scandinavian form of the female given name Martha, commonly used in countries such as Sweden, Norway, and Denmark.
  • D. Terézia Mora
    Terézia Mora is a Hungarian-born German writer and translator acclaimed for her innovative prose and contributions to contemporary German-language literature.
  • E. Milena Králíčková
    Milena Králíčková is a Czech academic and physician who serves as the rector of Charles University in Prague.
  • 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_69a88aa72d348190a9544bb5b8a4e71d completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbef0e2f0819080ca457fe3b8b419 completed March 7, 2026, 6 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae653de18481909c3521e060540a38 completed March 9, 2026, 6:14 a.m.
NEDg Description generation batch_69ae65d419048190ad723d21ab7f1cab completed March 9, 2026, 6:16 a.m.
NED2 Entity disambiguation (via description) batch_69ae666e71908190b50be2cac5bdfa28 completed March 9, 2026, 6:19 a.m.
Created at: March 4, 2026, 7:45 p.m.