Triple

T6611677
Position Surface form Disambiguated ID Type / Status
Subject Martina Navratilova E149251 entity
Predicate givenName P17 FINISHED
Object Martina
Martina is a feminine given name of Latin origin, commonly used in many European and Spanish-speaking countries.
 E599791 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: Martina | Statement: [Martina Navratilova, givenName, Martina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martina
Context triple: [Martina Navratilova, givenName, Martina]
  • 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. Renata
    Renata is a vampire in the Twilight series who serves the Volturi as a powerful bodyguard with a psychic ability to repel physical attacks.
  • C. Renata
    Renata is a young Venetian woman who becomes the poignant love interest of an aging American colonel in Ernest Hemingway’s novel "Across the River and Into the Trees."
  • D. Daniela
    Daniela is a feminine given name commonly used in many languages, often as the female form of Daniel.
  • E. Marta
    Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
  • 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: Martina
Triple: [Martina Navratilova, givenName, Martina]
Generated description
Martina is a feminine given name of Latin origin, commonly used in many European and Spanish-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Martina
Target entity description: Martina is a feminine given name of Latin origin, commonly used in many European and Spanish-speaking countries.
  • 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. Renata
    Renata is a young Venetian woman who becomes the poignant love interest of an aging American colonel in Ernest Hemingway’s novel "Across the River and Into the Trees."
  • C. Renata
    Renata is a vampire in the Twilight series who serves the Volturi as a powerful bodyguard with a psychic ability to repel physical attacks.
  • D. Daniela
    Daniela is a feminine given name commonly used in many languages, often as the female form of Daniel.
  • E. Marta
    Marta is a feminine given name commonly used in many European and Latin American countries, often considered a variant of the name Martha.
  • 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_69c687ebc680819094caf71faba2efe2 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6af3778a8819094e83afed7c6596f completed March 27, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cbd228fc8190852fac2308233765 completed March 27, 2026, 6:26 p.m.
NEDg Description generation batch_69c6cd428b988190b01311ca02f4dff3 completed March 27, 2026, 6:32 p.m.
NED2 Entity disambiguation (via description) batch_69c6cdcc10c08190aa98212bd17063a3 completed March 27, 2026, 6:34 p.m.
Created at: March 27, 2026, 1:57 p.m.