Triple

T5547339
Position Surface form Disambiguated ID Type / Status
Subject Enchanted E145439 entity
Predicate character P662 FINISHED
Object Queen Narissa
Queen Narissa is the primary villain of Disney’s live-action/animated film "Enchanted," a wicked sorceress and stepmother who schemes to retain her power over the kingdom of Andalasia.
E86510 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: Queen Narissa | Statement: [Enchanted, character, Queen Narissa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Queen Narissa
Context triple: [Enchanted, character, Queen Narissa]
  • A. Queen Ramonda
    Queen Ramonda is a regal Wakandan matriarch and mother of T’Challa in Marvel’s Black Panther franchise.
  • B. Queen Aoleon Joffer
    Queen Aoleon Joffer is a fictional royal consort of the Zamundan king in the comedy film "Coming to America," known for her regal demeanor and supportive role in the story.
  • C. Tamora
    Tamora is the vengeful Queen of the Goths and a central antagonist in William Shakespeare’s tragedy "Titus Andronicus."
  • D. Queen Bavmorda
    Queen Bavmorda is the ruthless and power-hungry sorceress-queen who serves as the primary villain in the fantasy film "Willow."
  • E. Queen Red
    Queen Red was one of the designated assault sub-sectors of Sword Beach used by Allied forces during the D-Day landings in World War II.
  • 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: Queen Narissa
Triple: [Enchanted, character, Queen Narissa]
Generated description
Queen Narissa is the primary villain of Disney’s live-action/animated film "Enchanted," a wicked sorceress and stepmother who schemes to retain her power over the kingdom of Andalasia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Queen Narissa
Target entity description: Queen Narissa is the primary villain of Disney’s live-action/animated film "Enchanted," a wicked sorceress and stepmother who schemes to retain her power over the kingdom of Andalasia.
  • A. Queen Ramonda
    Queen Ramonda is a regal Wakandan matriarch and mother of T’Challa in Marvel’s Black Panther franchise.
  • B. Queen Aoleon Joffer
    Queen Aoleon Joffer is a fictional royal consort of the Zamundan king in the comedy film "Coming to America," known for her regal demeanor and supportive role in the story.
  • C. Tamora
    Tamora is the vengeful Queen of the Goths and a central antagonist in William Shakespeare’s tragedy "Titus Andronicus."
  • D. Queen Bavmorda chosen
    Queen Bavmorda is the ruthless and power-hungry sorceress-queen who serves as the primary villain in the fantasy film "Willow."
  • E. Queen Red
    Queen Red was one of the designated assault sub-sectors of Sword Beach used by Allied forces during the D-Day landings in World War II.
  • F. None of above.

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_69c008fb879c81909f5bfa56fadc1d46 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01fe0244c8190aeb995f79f22a039 completed March 22, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0282ace308190a714685579f2a789 completed March 22, 2026, 5:34 p.m.
NEDg Description generation batch_69c0372e86c08190bf586256cab23d22 completed March 22, 2026, 6:38 p.m.
NED2 Entity disambiguation (via description) batch_69c038e5dccc8190a5e1ec45712c00a3 completed March 22, 2026, 6:45 p.m.
Created at: March 22, 2026, 3:35 p.m.