Triple

T14606539
Position Surface form Disambiguated ID Type / Status
Subject Kristi Gates E342843 entity
Predicate givenName P17 FINISHED
Object Kristi
Kristi is a feminine given name commonly used in English-speaking countries, often as a variant of Kristy or Christina.
 E1108294 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: Kristi | Statement: [Kristi Gates, givenName, Kristi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kristi
Context triple: [Kristi Gates, givenName, Kristi]
  • A. Krista
    Krista is a feminine given name, typically considered a variant of Christina and used in various European and English-speaking countries.
  • B. Kristy
    Kristy is a 2014 American horror-thriller film starring Haley Bennett as a college student terrorized by a violent cult during a holiday break on an almost-empty campus.
  • C. Kristen
    Kristen is the birth name of Kris Jenner, the American television personality and matriarch of the Kardashian–Jenner family.
  • D. 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.
  • 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: Kristi
Triple: [Kristi Gates, givenName, Kristi]
Generated description
Kristi is a feminine given name commonly used in English-speaking countries, often as a variant of Kristy or Christina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kristi
Target entity description: Kristi is a feminine given name commonly used in English-speaking countries, often as a variant of Kristy or Christina.
  • A. Krista
    Krista is a feminine given name, typically considered a variant of Christina and used in various European and English-speaking countries.
  • B. Kristy
    Kristy is a 2014 American horror-thriller film starring Haley Bennett as a college student terrorized by a violent cult during a holiday break on an almost-empty campus.
  • C. Kristen
    Kristen is the birth name of Kris Jenner, the American television personality and matriarch of the Kardashian–Jenner family.
  • D. 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.
  • 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

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_69d822dec68081908c2553145c4051dc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb44d327c8190a8d20568429d0f80 completed April 14, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd94d09e988190a2a2a1332397b412 completed May 8, 2026, 7:46 a.m.
NEDg Description generation batch_69fd9828129c8190bd7445e99dadc618 completed May 8, 2026, 8 a.m.
NED2 Entity disambiguation (via description) batch_69fd98cf0bcc81909dac826a32daaf04 completed May 8, 2026, 8:03 a.m.
Created at: April 10, 2026, 1:25 a.m.