Triple

T477179
Position Surface form Disambiguated ID Type / Status
Subject For Whom the Bell Tolls E9086 entity
Predicate mainCharacter P1183 FINISHED
Object 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.
 E65513 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: María | Statement: [For Whom the Bell Tolls, mainCharacter, María]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: María
Context triple: [For Whom the Bell Tolls, mainCharacter, María]
  • A. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • B. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • C. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • D. Caterina
    Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
  • E. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • 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: María
Triple: [For Whom the Bell Tolls, mainCharacter, María]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: María
Target entity description: 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.
  • A. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • B. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • C. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • D. Caterina
    Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
  • E. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • 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_69a2e7ff81708190b0507a24a997232c completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2f03cc63881908e75b457804cb858 completed Feb. 28, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4b23a3ddc8190a685a63923d1d175 completed March 1, 2026, 9:40 p.m.
NEDg Description generation batch_69a4b2c975ec8190bc16f146f89c3f05 completed March 1, 2026, 9:42 p.m.
NED2 Entity disambiguation (via description) batch_69a4b343f8f88190bd011924dd485bef completed March 1, 2026, 9:44 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.