Triple

T7548539
Position Surface form Disambiguated ID Type / Status
Subject Carmen Basilio E178468 entity
Predicate givenName P17 FINISHED
Object Carmen
Carmen is a given name used for both males and females in various cultures, notably in Spanish- and Italian-speaking countries.
 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 Basilio, givenName, Carmen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Carmen
Context triple: [Carmen Basilio, givenName, Carmen]
  • A. Carmen
    Carmen is a key character in the dark fantasy film "Pan’s Labyrinth," serving as the pregnant mother whose fragile health and marriage to a brutal captain frame the story’s wartime and familial tensions.
  • B. Carmen
    Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
  • C. 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.
  • D. Carmen
    Carmen is a landlocked agricultural municipality in the province of North Cotabato on the island of Mindanao in the Philippines.
  • E. 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.
  • 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 Basilio, givenName, Carmen]
Generated description
Carmen is a given name used for both males and females in various cultures, notably in Spanish- and Italian-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Carmen
Target entity description: Carmen is a given name used for both males and females in various cultures, notably in Spanish- and Italian-speaking countries.
  • 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 famous opera by Georges Bizet, renowned for its passionate music and tragic story centered on the free-spirited gypsy Carmen.
  • C. 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.
  • D. Carmen
    Carmen is a 1983 Spanish musical drama film directed by Carlos Saura that reimagines the classic Bizet opera through flamenco dance.
  • E. Carmen
    Carmen is a municipality in the province of Cebu in the Philippines, known for its agricultural economy and proximity to coastal and upland attractions.
  • 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_69c69f2cbe08819088f9eb0c03ef529b completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f89b9afc8190b3e61a8e2cea7ad7 completed March 27, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84f2b0de48190a854b4d4935c2254 completed March 28, 2026, 9:59 p.m.
NEDg Description generation batch_69c85188f2648190abf2224cf272f5ac completed March 28, 2026, 10:09 p.m.
NED2 Entity disambiguation (via description) batch_69c85238850081908fc50fa9c0f6c767 completed March 28, 2026, 10:12 p.m.
Created at: March 27, 2026, 3:49 p.m.