Triple

T3554459
Position Surface form Disambiguated ID Type / Status
Subject Christina E75185 entity
Predicate hasVariant P455 FINISHED
Object Kristin
Kristin is a feminine given name, commonly used in various European and English-speaking countries, often considered a variant of Christina or Christine.
 E101122 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: Kristin | Statement: [Christina, hasVariant, Kristin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kristin
Context triple: [Christina, hasVariant, Kristin]
  • A. Kristin
    Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
  • B. Kristin
    Kristin is the given name of the acclaimed British-French actress Kristin Scott Thomas, known for her roles in films such as "The English Patient" and "Four Weddings and a Funeral."
  • C. 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.
  • D. Kristen
    Kristen is the birth name of Kris Jenner, the American television personality and matriarch of the Kardashian–Jenner family.
  • E. Kristen
    Kristen is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
  • 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: Kristin
Triple: [Christina, hasVariant, Kristin]
Generated description
Kristin is a feminine given name, commonly used in various European and English-speaking countries, often considered a variant of Christina or Christine.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kristin
Target entity description: Kristin is a feminine given name, commonly used in various European and English-speaking countries, often considered a variant of Christina or Christine.
  • A. Kristin chosen
    Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
  • B. Kristin
    Kristin is the given name of the acclaimed British-French actress Kristin Scott Thomas, known for her roles in films such as "The English Patient" and "Four Weddings and a Funeral."
  • C. 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.
  • D. Kristen
    Kristen is the birth name of Kris Jenner, the American television personality and matriarch of the Kardashian–Jenner family.
  • E. Kristen
    Kristen is a feminine given name commonly used in English-speaking countries, often associated with notable figures in entertainment and public life.
  • F. None of above.

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_69ad85d33c6c819081d5ac1df13b5680 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc05549d88190acdebdd542ea1a67 completed March 8, 2026, 6:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69b432fdda488190a31f74e80685c121 completed March 13, 2026, 3:53 p.m.
NEDg Description generation batch_69b436f57b2c8190b010197b01859980 completed March 13, 2026, 4:10 p.m.
NED2 Entity disambiguation (via description) batch_69b437493384819084a7213fe754526b completed March 13, 2026, 4:11 p.m.
Created at: March 8, 2026, 3:20 p.m.