Triple

T2666772
Position Surface form Disambiguated ID Type / Status
Subject Carlos E55653 entity
Predicate hasFeminineForm P1613 FINISHED
Object Carlota
Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
 E293081 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: Carlota | Statement: [Carlos, hasFeminineForm, Carlota]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Carlota
Context triple: [Carlos, hasFeminineForm, Carlota]
  • A. La Carlota
    La Carlota is a component city in the province of Negros Occidental in the Philippines, known for its agricultural economy and historic sugar industry.
  • B. Juana
    Juana is the Spanish feminine given name equivalent to Jane, commonly used in Spanish-speaking cultures.
  • C. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • D. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • E. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • 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: Carlota
Triple: [Carlos, hasFeminineForm, Carlota]
Generated description
Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Carlota
Target entity description: Carlota is the feminine given name corresponding to Carlos, commonly used in Spanish- and Portuguese-speaking cultures.
  • A. La Carlota
    La Carlota is a component city in the province of Negros Occidental in the Philippines, known for its agricultural economy and historic sugar industry.
  • B. Juana
    Juana is the Spanish feminine given name equivalent to Jane, commonly used in Spanish-speaking cultures.
  • C. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • D. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • E. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • 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_69ab49e54de48190be708cd1cf8be073 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd97040e48190b0a87489f108810e completed March 7, 2026, 7:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69afb6775b008190a59e480516c3ef41 completed March 10, 2026, 6:13 a.m.
NEDg Description generation batch_69afb75ea498819089c79e63052e9696 completed March 10, 2026, 6:17 a.m.
NED2 Entity disambiguation (via description) batch_69afb83ba6dc8190931d691d3e354bd7 completed March 10, 2026, 6:20 a.m.
Created at: March 6, 2026, 9:54 p.m.