Triple

T3301116
Position Surface form Disambiguated ID Type / Status
Subject The Pursuit of Happyness E69333 entity
Predicate character P662 FINISHED
Object Linda
Linda is a supporting character in the film "The Pursuit of Happyness," depicted as Chris Gardner’s struggling and increasingly distant partner amid the family’s financial hardships.
 E359195 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: Linda | Statement: [The Pursuit of Happyness, character, Linda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Linda
Context triple: [The Pursuit of Happyness, character, Linda]
  • A. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • B. Laurie
    Laurie is a charming, wealthy, and impulsive young man who becomes a close friend and would-be suitor to the March sisters in Louisa May Alcott’s novel "Little Women."
  • C. Lillian
    Lillian is the given name of Lil Hardin Armstrong, a pioneering American jazz pianist, composer, bandleader, and second wife of Louis Armstrong.
  • D. Diane
    Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
  • E. Sandra
    Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
  • 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: Linda
Triple: [The Pursuit of Happyness, character, Linda]
Generated description
Linda is a supporting character in the film "The Pursuit of Happyness," depicted as Chris Gardner’s struggling and increasingly distant partner amid the family’s financial hardships.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Linda
Target entity description: Linda is a supporting character in the film "The Pursuit of Happyness," depicted as Chris Gardner’s struggling and increasingly distant partner amid the family’s financial hardships.
  • A. Linda
    Linda is a feminine given name of Germanic origin that became widely used in English-speaking countries in the 20th century.
  • B. Laurie
    Laurie is a charming, wealthy, and impulsive young man who becomes a close friend and would-be suitor to the March sisters in Louisa May Alcott’s novel "Little Women."
  • C. Lillian
    Lillian is the given name of Lil Hardin Armstrong, a pioneering American jazz pianist, composer, bandleader, and second wife of Louis Armstrong.
  • D. Diane
    Diane is a feminine given name of Latin origin, derived from the name of the Roman goddess Diana.
  • E. Sandra
    Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
  • 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_69ad859e529c8190a404273f53cb487d completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0a7d224819080d9a638e08bb8a8 completed March 8, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3609bd0d48190838df7dd7cbca159 completed March 13, 2026, 12:55 a.m.
NEDg Description generation batch_69b3627bb1e48190a51b3bafccefa03e completed March 13, 2026, 1:03 a.m.
NED2 Entity disambiguation (via description) batch_69b362ceeab481909c92fb885f477d3c completed March 13, 2026, 1:05 a.m.
Created at: March 8, 2026, 3:11 p.m.