Triple

T22184996
Position Surface form Disambiguated ID Type / Status
Subject Le Crime de Monsieur Lange E548270 entity
Predicate castMember P1668 FINISHED
Object Florelle
Florelle was a French actress and singer of the early 20th century, known for her roles in classic French cinema and on the Parisian stage.
 E1522987 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: Florelle | Statement: [Le Crime de Monsieur Lange, castMember, Florelle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Florelle
Context triple: [Le Crime de Monsieur Lange, castMember, Florelle]
  • A. Fleur
    Fleur is a feminine given name of French origin meaning "flower," often used as a middle name in English-speaking countries.
  • B. Feliche
    Feliche is a character in John Marston’s late-Elizabethan play "Antonio and Mellida," a satirical drama first performed around 1599.
  • C. Olivette
    Olivette is a diminutive given name derived from Olive, often associated with peace and nature.
  • D. Clémentine
    Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
  • E. Arabelle
    Arabelle is a feminine given name, typically considered a variant of Arabella, used in various English-speaking and European 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: Florelle
Triple: [Le Crime de Monsieur Lange, castMember, Florelle]
Generated description
Florelle was a French actress and singer of the early 20th century, known for her roles in classic French cinema and on the Parisian stage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Florelle
Target entity description: Florelle was a French actress and singer of the early 20th century, known for her roles in classic French cinema and on the Parisian stage.
  • A. Fleur
    Fleur is a feminine given name of French origin meaning "flower," often used as a middle name in English-speaking countries.
  • B. Feliche
    Feliche is a character in John Marston’s late-Elizabethan play "Antonio and Mellida," a satirical drama first performed around 1599.
  • C. Olivette
    Olivette is a diminutive given name derived from Olive, often associated with peace and nature.
  • D. Clémentine
    Clémentine is a feminine given name of French origin, commonly used in Francophone countries and beyond.
  • E. Arabelle
    Arabelle is a feminine given name, typically considered a variant of Arabella, used in various English-speaking and European 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_69e11e3e0c7c8190b30d278845e2497e completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12aa823888190829368de6db4aa91 completed April 28, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a9ecfd0908190938f604f19417e93 completed May 18, 2026, 5:08 a.m.
NEDg Description generation batch_6a0a9f5a0a2c8190b0a8a4c56938cc02 completed May 18, 2026, 5:10 a.m.
NED2 Entity disambiguation (via description) batch_6a0aa01988b481909fe3bf966feb5916 completed May 18, 2026, 5:14 a.m.
Created at: April 16, 2026, 8:35 p.m.