Triple

T660995
Position Surface form Disambiguated ID Type / Status
Subject Like Water for Chocolate E11754 entity
Predicate mainCharacter P1183 FINISHED
Object Rosaura
Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
 E91785 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: Rosaura | Statement: [Like Water for Chocolate, mainCharacter, Rosaura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Rosaura
Context triple: [Like Water for Chocolate, mainCharacter, Rosaura]
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • C. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • D. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • E. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • 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: Rosaura
Triple: [Like Water for Chocolate, mainCharacter, Rosaura]
Generated description
Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Rosaura
Target entity description: Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • A. Clementina
    Clementina is a feminine given name, often considered a variant of Clementine, used in various European and Latin American cultures.
  • B. Pilar
    Pilar is a strong-willed, perceptive Spanish guerrilla fighter who plays a central role in Ernest Hemingway’s novel "For Whom the Bell Tolls."
  • C. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • D. Paola
    Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
  • E. Francisca
    Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fa954988190841740a587ace466 completed March 1, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69a66d8f4a908190bc4cf5e1a6e46628 completed March 3, 2026, 5:11 a.m.
NEDg Description generation batch_69a66e16c7bc8190a6aea054bac99c4c completed March 3, 2026, 5:13 a.m.
NED2 Entity disambiguation (via description) batch_69a66e7dd53c8190bdb3768abd80ba8d completed March 3, 2026, 5:15 a.m.
Created at: March 1, 2026, 7:36 p.m.