Triple

T9616939
Position Surface form Disambiguated ID Type / Status
Subject Marshmallow E232241 entity
Predicate loyalTo P1201 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: [Marshmallow, loyalTo, Elsa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elsa
Context triple: [Marshmallow, loyalTo, 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_69ca84867bb88190b4b57dd5a56d5691 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9aaf3a088190a00a7750c25b6c42 completed April 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18225f9508190bde23b9d2a40bccc completed April 4, 2026, 9:27 p.m.
Created at: March 30, 2026, 8:09 p.m.