Triple

T657140
Position Surface form Disambiguated ID Type / Status
Subject Tina Fey E11671 entity
Predicate nickname P55 FINISHED
Object Tina
Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
 E115406 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: Tina | Statement: [Tina Fey, nickname, Tina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tina
Context triple: [Tina Fey, nickname, Tina]
  • 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. 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.
  • C. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • D. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • E. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • 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: Tina
Triple: [Tina Fey, nickname, Tina]
Generated description
Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tina
Target entity description: Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
  • 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. 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.
  • C. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • D. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • E. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49f4e87408190b5276d2b913d0426 completed March 1, 2026, 8:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac1ccab5bc819099c0f060147c8f27 completed March 7, 2026, 12:40 p.m.
NEDg Description generation batch_69ac1d5960408190bf7dd3b8b64709db completed March 7, 2026, 12:43 p.m.
NED2 Entity disambiguation (via description) batch_69ac1dc56f7481909eb1ffb6b24db39f completed March 7, 2026, 12:44 p.m.
Created at: March 1, 2026, 7:36 p.m.