Triple

T613162
Position Surface form Disambiguated ID Type / Status
Subject Frances E12143 entity
Predicate cognateWith P2525 FINISHED
Object Francisca
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
 E88236 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: Francisca | Statement: [Frances, cognateWith, Francisca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Francisca
Context triple: [Frances, cognateWith, Francisca]
  • A. 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.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Caterina
    Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
  • D. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • E. Teresa di Blasco
    Teresa di Blasco was the wife of Italian Enlightenment jurist and philosopher Cesare Beccaria, known for his pioneering work on criminal justice reform.
  • 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: Francisca
Triple: [Frances, cognateWith, Francisca]
Generated description
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Francisca
Target entity description: Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • A. 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.
  • B. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • C. Caterina
    Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
  • D. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • E. Teresa di Blasco
    Teresa di Blasco was the wife of Italian Enlightenment jurist and philosopher Cesare Beccaria, known for his pioneering work on criminal justice reform.
  • 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_69a493309df48190a327f748e88049a6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49e08dbf88190ab050078a63e266b completed March 1, 2026, 8:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a64a4ba2d88190969e7c777a1bbfd7 completed March 3, 2026, 2:41 a.m.
NEDg Description generation batch_69a64e773be08190abd15ff6ad35f37b completed March 3, 2026, 2:59 a.m.
NED2 Entity disambiguation (via description) batch_69a64edbfbe08190b49d26d572e3a484 completed March 3, 2026, 3 a.m.
Created at: March 1, 2026, 7:35 p.m.