Triple

T522911
Position Surface form Disambiguated ID Type / Status
Subject Think Like a Man E10856 entity
Predicate mainCharacter P1183 FINISHED
Object Kristen
Kristen is a central female character in the romantic comedy film "Think Like a Man," whose love life and personal growth are explored through the movie’s ensemble relationship dynamics.
 E83981 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: Kristen | Statement: [Think Like a Man, mainCharacter, Kristen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kristen
Context triple: [Think Like a Man, mainCharacter, Kristen]
  • A. Kathryn
    Kathryn is a feminine given name, commonly considered a variant spelling of Katherine/Catherine.
  • B. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • C. Christina
    Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
  • D. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • E. Kathy
    Kathy is the given name of Kathy Hochul, the 57th governor of New York and the first woman to hold that office.
  • 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: Kristen
Triple: [Think Like a Man, mainCharacter, Kristen]
Generated description
Kristen is a central female character in the romantic comedy film "Think Like a Man," whose love life and personal growth are explored through the movie’s ensemble relationship dynamics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kristen
Target entity description: Kristen is a central female character in the romantic comedy film "Think Like a Man," whose love life and personal growth are explored through the movie’s ensemble relationship dynamics.
  • A. Kathryn
    Kathryn is a feminine given name, commonly considered a variant spelling of Katherine/Catherine.
  • B. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • C. Christina
    Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
  • D. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • E. Kathy
    Kathy is the given name of Kathy Hochul, the 57th governor of New York and the first woman to hold that office.
  • 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_69a5dc8df9708190976967ad4e45a597 completed March 2, 2026, 6:53 p.m.
NEDg Description generation batch_69a5de11a0ac81909247a98bbc317cf7 completed March 2, 2026, 6:59 p.m.
NED2 Entity disambiguation (via description) batch_69a5febeeb908190a42d468edfe985ef completed March 2, 2026, 9:18 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.