Triple

T9484977
Position Surface form Disambiguated ID Type / Status
Subject Royal Sommerhus E228736 entity
Predicate featuresCharacter P626 FINISHED
Object Elsa E44923 NE FINISHED

How this triple was built (2 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: Elsa | Statement: [Royal Sommerhus, featuresCharacter, Elsa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elsa
Context triple: [Royal Sommerhus, featuresCharacter, Elsa]
  • A. Elsa chosen
    Elsa is a feminine given name of Germanic origin, widely recognized today through its use for the main character in Disney's animated film "Frozen."
  • B. Elsa Bannister
    Elsa Bannister is the enigmatic, manipulative femme fatale at the center of Orson Welles's film noir "The Lady from Shanghai."
  • C. Elsa Viveca Torstensdotter Lindfors
    Elsa Viveca Torstensdotter Lindfors, known professionally as Viveca Lindfors, was a Swedish-American actress celebrated for her work in European and Hollywood films and on stage during the mid-20th century.
  • D. Anna (Frozen)
    Anna (Frozen) is the optimistic and fearless younger princess of Arendelle from Disney's Frozen franchise, known for her adventurous spirit, deep love for her sister Elsa, and determination to save their kingdom.
  • E. Anna and Elsa
    Anna and Elsa are the popular sister protagonists from Disney's animated film "Frozen," known for their roles as the Snow Queen and the princess of Arendelle.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca84730a5081908de282651019bf2f completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd804e278c8190b1f869158075cd52 completed April 1, 2026, 8:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69d12d1326b8819084c6d9490b7a96dc completed April 4, 2026, 3:24 p.m.
Created at: March 30, 2026, 7:55 p.m.