Triple

T2451655
Position Surface form Disambiguated ID Type / Status
Subject Kristin Scott Thomas E53718 entity
Predicate givenName P17 FINISHED
Object 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."
 E268971 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: [Kristin Scott Thomas, givenName, Kristin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kristin
Context triple: [Kristin Scott Thomas, givenName, Kristin]
  • A. Kristin
    Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
  • B. 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.
  • C. Kirsten
    Kirsten is the first name of Kirsten Gillibrand, a prominent American politician and U.S. Senator from New York.
  • D. Kathryn
    Kathryn is a feminine given name, commonly considered a variant spelling of Katherine/Catherine.
  • E. Karin
    Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
  • 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: [Kristin Scott Thomas, givenName, Kristin]
Generated description
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."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kristin
Target entity description: 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."
  • A. Kristin
    Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
  • B. 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.
  • C. Kirsten
    Kirsten is the first name of Kirsten Gillibrand, a prominent American politician and U.S. Senator from New York.
  • D. Kathryn
    Kathryn is a feminine given name, commonly considered a variant spelling of Katherine/Catherine.
  • E. Karin
    Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
  • 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_69ab495d227c8190b26ae6548eeb1019 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd0f52524819088b00009c9dd1823 completed March 7, 2026, 7:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0c544788190bb0e8c5ae4a32ff0 completed March 9, 2026, 4:09 p.m.
NEDg Description generation batch_69aef53740508190893b14bb1b411a30 completed March 9, 2026, 4:28 p.m.
NED2 Entity disambiguation (via description) batch_69aef9594024819088e7afc0e64429ff completed March 9, 2026, 4:46 p.m.
Created at: March 6, 2026, 9:43 p.m.