Triple

T522906
Position Surface form Disambiguated ID Type / Status
Subject Think Like a Man E10856 entity
Predicate mainCharacter P1183 FINISHED
Object Lauren
Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
 E90841 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: Lauren | Statement: [Think Like a Man, mainCharacter, Lauren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lauren
Context triple: [Think Like a Man, mainCharacter, Lauren]
  • A. Laurene
    Laurene is the first name of Laurene Powell Jobs, an American businesswoman, philanthropist, and widow of Apple co-founder Steve Jobs.
  • B. Becky
    Becky is a common English feminine given name, typically used as a diminutive of Rebecca.
  • C. Courtney
    Courtney is a surname of Irish origin that is also commonly used as a given name.
  • D. Chloe
    Chloe is the birth name of Nobel Prize–winning American novelist Toni Morrison, renowned for her powerful explorations of African American life and history.
  • E. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 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: Lauren
Triple: [Think Like a Man, mainCharacter, Lauren]
Generated description
Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lauren
Target entity description: Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
  • A. Laurene
    Laurene is the first name of Laurene Powell Jobs, an American businesswoman, philanthropist, and widow of Apple co-founder Steve Jobs.
  • B. Becky
    Becky is a common English feminine given name, typically used as a diminutive of Rebecca.
  • C. Courtney
    Courtney is a surname of Irish origin that is also commonly used as a given name.
  • D. Chloe
    Chloe is the birth name of Nobel Prize–winning American novelist Toni Morrison, renowned for her powerful explorations of African American life and history.
  • E. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 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_69a2e84b16c4819088d284c47c3a7968 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1b4f01881908b408357ff113308 completed Feb. 28, 2026, 1:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a66662e848819095aca23ceb5eeef2 completed March 3, 2026, 4:41 a.m.
NEDg Description generation batch_69a666ded0288190a43e8a13db4f6914 completed March 3, 2026, 4:43 a.m.
NED2 Entity disambiguation (via description) batch_69a667b8de2c819092f9a4c10abeeb56 completed March 3, 2026, 4:46 a.m.
Created at: Feb. 28, 2026, 1:12 p.m.