Triple

T12232165
Position Surface form Disambiguated ID Type / Status
Subject A-Force E291499 entity
Predicate member P10 FINISHED
Object Pixie
Pixie is a Marvel Comics mutant superhero known for her pink hair, insect-like wings, and teleportation magic, who has served on various X-Men–related teams.
 E970740 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: Pixie | Statement: [A-Force, member, Pixie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pixie
Context triple: [A-Force, member, Pixie]
  • A. Pixie
    Pixie is the stage nickname of English singer, songwriter, and actress Pixie Lott, known for her pop hits in the late 2000s and 2010s.
  • B. Pixie
    Pixie is a small, clever cartoon mouse from the classic Hanna-Barbera series "The Huckleberry Hound Show," often seen teaming up with his fellow mouse Dixie in comedic escapades.
  • C. Chinky the pixie
    Chinky the pixie is a mischievous magical creature who accompanies children on fantastical flying adventures in Enid Blyton’s Wishing-Chair stories.
  • D. Silky the Fairy
    Silky the Fairy is a kind, gentle fairy who lives in the Magic Faraway Tree and helps guide the children through its many strange and magical lands.
  • E. Pippy
    Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
  • 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: Pixie
Triple: [A-Force, member, Pixie]
Generated description
Pixie is a Marvel Comics mutant superhero known for her pink hair, insect-like wings, and teleportation magic, who has served on various X-Men–related teams.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pixie
Target entity description: Pixie is a Marvel Comics mutant superhero known for her pink hair, insect-like wings, and teleportation magic, who has served on various X-Men–related teams.
  • A. Pixie
    Pixie is the stage nickname of English singer, songwriter, and actress Pixie Lott, known for her pop hits in the late 2000s and 2010s.
  • B. Pixie
    Pixie is a small, clever cartoon mouse from the classic Hanna-Barbera series "The Huckleberry Hound Show," often seen teaming up with his fellow mouse Dixie in comedic escapades.
  • C. Chinky the pixie
    Chinky the pixie is a mischievous magical creature who accompanies children on fantastical flying adventures in Enid Blyton’s Wishing-Chair stories.
  • D. Silky the Fairy
    Silky the Fairy is a kind, gentle fairy who lives in the Magic Faraway Tree and helps guide the children through its many strange and magical lands.
  • E. Pippy
    Pippy is an educational programming activity for the Sugar learning platform that lets children explore and write simple Python programs.
  • 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_69d6ab668acc8190963ba424049d6aee completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91ca45bd48190b8b7f6b29b6bb25b completed April 10, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60aad2d488190ba36588e3376ca1a completed May 2, 2026, 2:31 p.m.
NEDg Description generation batch_69f60bdd8d508190813178ff4c77afcf completed May 2, 2026, 2:36 p.m.
NED2 Entity disambiguation (via description) batch_69f60c67c680819087630d190d0a008f completed May 2, 2026, 2:38 p.m.
Created at: April 8, 2026, 9:51 p.m.