Triple

T8446461
Position Surface form Disambiguated ID Type / Status
Subject Forest of Brocéliande E199688 entity
Predicate associatedWith P37 FINISHED
Object Viviane
Viviane is a legendary enchantress of Arthurian romance, often identified as the Lady of the Lake and known for her role in mentoring and imprisoning the wizard Merlin.
 E741555 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: Viviane | Statement: [Forest of Brocéliande, associatedWith, Viviane]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Viviane
Context triple: [Forest of Brocéliande, associatedWith, Viviane]
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Alessandra
    Alessandra is an Italian politician, former actress, and granddaughter of Benito Mussolini.
  • C. Liliane
    Liliane is a feminine given name of French origin, notably borne by French heiress and businesswoman Liliane Bettencourt.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Béatrix
    Béatrix is a novel by Honoré de Balzac that forms part of his larger La Comédie humaine cycle, depicting the complexities of love and society in 19th-century France.
  • 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: Viviane
Triple: [Forest of Brocéliande, associatedWith, Viviane]
Generated description
Viviane is a legendary enchantress of Arthurian romance, often identified as the Lady of the Lake and known for her role in mentoring and imprisoning the wizard Merlin.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Viviane
Target entity description: Viviane is a legendary enchantress of Arthurian romance, often identified as the Lady of the Lake and known for her role in mentoring and imprisoning the wizard Merlin.
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Alessandra
    Alessandra is an Italian politician, former actress, and granddaughter of Benito Mussolini.
  • C. Liliane
    Liliane is a feminine given name of French origin, notably borne by French heiress and businesswoman Liliane Bettencourt.
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Béatrix
    Béatrix is a novel by Honoré de Balzac that forms part of his larger La Comédie humaine cycle, depicting the complexities of love and society in 19th-century France.
  • 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_69ca83170f9081909cd98f55614c6476 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe3152a3c819092efdeab718def7a completed March 31, 2026, 3:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6cf9368081909cad61cdf6156a0e completed April 2, 2026, 1:19 p.m.
NEDg Description generation batch_69ce6ec1e74081908fc235ffd13ef301 completed April 2, 2026, 1:27 p.m.
NED2 Entity disambiguation (via description) batch_69ce6fe13a00819095f5bec408435426 completed April 2, 2026, 1:32 p.m.
Created at: March 30, 2026, 6:09 p.m.