Triple

T2166848
Position Surface form Disambiguated ID Type / Status
Subject Tamara Geva E46928 entity
Predicate givenName P17 FINISHED
Object Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
 E250443 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: Tamara | Statement: [Tamara Geva, givenName, Tamara]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tamara
Context triple: [Tamara Geva, givenName, Tamara]
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Tessa
    Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
  • C. Joanna
    Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
  • D. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • E. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • 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: Tamara
Triple: [Tamara Geva, givenName, Tamara]
Generated description
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tamara
Target entity description: Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Tessa
    Tessa is a feminine given name commonly used in English-speaking countries, often as a diminutive of Theresa or Therese.
  • C. Joanna
    Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
  • D. Teressa
    Teressa is a Nicobarese language variety spoken by the indigenous community on Teressa Island in India’s Nicobar archipelago.
  • E. Sonia
    Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
  • 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_69a88a184cbc8190877791f6552c2484 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbeab223881908aaa2bc4f85329cc completed March 7, 2026, 5:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71b142208190a09c8e459b200ecb completed March 9, 2026, 7:07 a.m.
NEDg Description generation batch_69ae71f7263c819086690d5cf8b2e1a7 completed March 9, 2026, 7:08 a.m.
NED2 Entity disambiguation (via description) batch_69ae734a22f48190a437cbc57c659d38 completed March 9, 2026, 7:14 a.m.
Created at: March 4, 2026, 7:45 p.m.