Triple

T2918346
Position Surface form Disambiguated ID Type / Status
Subject Greta Garbo E78659 entity
Predicate notableWork P4 FINISHED
Object Camille
Camille is a classic 1936 romantic drama film starring Greta Garbo as a tragic Parisian courtesan.
 E309907 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: Camille | Statement: [Greta Garbo, notableWork, Camille]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Camille
Context triple: [Greta Garbo, notableWork, Camille]
  • A. Camille
    Camille is a French given name used for both males and females, historically associated with figures such as the revolutionary journalist Camille Desmoulins.
  • B. Camille Roux
    Camille Roux was an artist associated with the Impressionist movement who participated in the historic Impressionist exhibitions in late 19th-century France.
  • C. Camille (The Woman in the Green Dress)
    "Camille (The Woman in the Green Dress)" is an 1866 oil painting by Claude Monet portraying his future wife Camille Doncieux in an elegant, fashionable gown, notable for helping establish his early reputation in the Paris art world.
  • D. Marguerite
    Marguerite is a French given name, equivalent to Margaret, commonly used for women and also meaning "daisy" in French.
  • E. Jeanne
    Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
  • 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: Camille
Triple: [Greta Garbo, notableWork, Camille]
Generated description
Camille is a classic 1936 romantic drama film starring Greta Garbo as a tragic Parisian courtesan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Camille
Target entity description: Camille is a classic 1936 romantic drama film starring Greta Garbo as a tragic Parisian courtesan.
  • A. Camille
    Camille is a French given name used for both males and females, historically associated with figures such as the revolutionary journalist Camille Desmoulins.
  • B. Camille Roux
    Camille Roux was an artist associated with the Impressionist movement who participated in the historic Impressionist exhibitions in late 19th-century France.
  • C. Camille (The Woman in the Green Dress)
    "Camille (The Woman in the Green Dress)" is an 1866 oil painting by Claude Monet portraying his future wife Camille Doncieux in an elegant, fashionable gown, notable for helping establish his early reputation in the Paris art world.
  • D. Marguerite
    Marguerite is a French given name, equivalent to Margaret, commonly used for women and also meaning "daisy" in French.
  • E. Jeanne
    Jeanne was a common French female given name historically borne by notable figures such as queens, saints, and writers.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad96a41b4c81909d8ace8ab270ed3c completed March 8, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0562c5b5081908026b3f590b03aca completed March 10, 2026, 5:34 p.m.
NEDg Description generation batch_69b0613dfb048190b08b01837088b9dd completed March 10, 2026, 6:21 p.m.
NED2 Entity disambiguation (via description) batch_69b06514562881909d3b08af898406f7 completed March 10, 2026, 6:38 p.m.
Created at: March 8, 2026, 2:54 p.m.