Triple

T22787244
Position Surface form Disambiguated ID Type / Status
Subject Carmen Laffón E564002 entity
Predicate givenName P17 FINISHED
Object Carmen
Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking countries and associated with cultural and literary figures.
E358979 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: Carmen | Statement: [Carmen Laffón, givenName, Carmen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carmen
Context triple: [Carmen Laffón, givenName, Carmen]
  • A. Carmen
    Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
  • B. Carmen
    Carmen is a supporting character in Jim Jarmusch’s film "Broken Flowers," connected to the protagonist’s journey to revisit women from his past.
  • C. Carmen
    Carmen is a key character in the 2012 ensemble comedy-drama film "Darling Companion," which centers on family relationships and the search for a lost dog.
  • D. Carmen
    Carmen is a character from the animated series "The Amazing World of Gumball," known as a strict, rule-abiding cactus who attends Elmore Junior High.
  • E. Carmen
    Carmen is Francesco Rosi’s 1984 film adaptation of Bizet’s famous opera, noted for its realistic setting and cinematic interpretation of the classic tragic love story.
  • 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: Carmen
Triple: [Carmen Laffón, givenName, Carmen]
Generated description
Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking countries and associated with cultural and literary figures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Carmen
Target entity description: Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking countries and associated with cultural and literary figures.
  • A. Carmen chosen
    Carmen is a feminine given name of Latin origin, widely used in Spanish-speaking cultures and beyond.
  • B. Carmen
    Carmen is a surname most notably associated with American singer-songwriter Eric Carmen, known for his work both as a solo artist and with the Raspberries.
  • C. Carmen
    Carmen is a famous opera by Georges Bizet, renowned for its passionate music and tragic story centered on the free-spirited gypsy Carmen.
  • D. Carmen
    Carmen is a central district of San José, Costa Rica, known for its urban character and role in the capital’s administrative and commercial life.
  • E. Carmen
    Carmen is a central character in the 2012 Spanish silent film "Blancanieves," a dark, flamenco-infused reimagining of the Snow White fairy tale.
  • 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_69e2455500788190b4b33030461f3bbd completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17c32de6481909ef358d16de98496 completed April 29, 2026, 3:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b982d15948190948cf6eeb33fa34e completed May 18, 2026, 10:52 p.m.
NEDg Description generation batch_6a0b9970f0388190a71f234f8022fc3e completed May 18, 2026, 10:57 p.m.
NED2 Entity disambiguation (via description) batch_6a0b9a1129c8819084163353accc7e14 completed May 18, 2026, 11 p.m.
Created at: April 17, 2026, 3:29 p.m.