Triple

T21780764
Position Surface form Disambiguated ID Type / Status
Subject Bonjour Tristesse (film) E537704 entity
Predicate character P662 FINISHED
Object Cécile
Cécile is the introspective, emotionally conflicted teenage protagonist of the French film "Bonjour Tristesse," whose relationships and moral choices drive the story’s drama.
 E1503105 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: Cécile | Statement: [Bonjour Tristesse (film), character, Cécile]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Cécile
Context triple: [Bonjour Tristesse (film), character, Cécile]
  • A. Cécile
    Cécile is the sensitive and central protagonist of the French film "Cible émouvante," around whom the story’s emotional and narrative developments revolve.
  • B. Cécile
    Cécile is a character in Jacques Demy’s 1961 French musical film "Lola."
  • C. Clélia
    Clélia is a feminine given name, primarily used in French-speaking countries and derived from the Latin name Clelia.
  • D. Clémentine
    Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
  • E. Bénédicte
    Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
  • 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: Cécile
Triple: [Bonjour Tristesse (film), character, Cécile]
Generated description
Cécile is the introspective, emotionally conflicted teenage protagonist of the French film "Bonjour Tristesse," whose relationships and moral choices drive the story’s drama.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Cécile
Target entity description: Cécile is the introspective, emotionally conflicted teenage protagonist of the French film "Bonjour Tristesse," whose relationships and moral choices drive the story’s drama.
  • A. Cécile
    Cécile is the sensitive and central protagonist of the French film "Cible émouvante," around whom the story’s emotional and narrative developments revolve.
  • B. Cécile
    Cécile is a character in Jacques Demy’s 1961 French musical film "Lola."
  • C. Clélia
    Clélia is a feminine given name, primarily used in French-speaking countries and derived from the Latin name Clelia.
  • D. Clémentine
    Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
  • E. Bénédicte
    Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
  • 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_69e0c470759c819094a215757113562b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f0462cae6481908d3e7f71683d8921 completed April 28, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0a45d4af6481909743ff2a2a244f2c completed May 17, 2026, 10:48 p.m.
NEDg Description generation batch_6a0a46a16de88190a930f98921d41239 completed May 17, 2026, 10:52 p.m.
NED2 Entity disambiguation (via description) batch_6a0a471f6d3481908108ceea4ff1c47b completed May 17, 2026, 10:54 p.m.
Created at: April 16, 2026, 6:52 p.m.