Triple

T30398669
Position Surface form Disambiguated ID Type / Status
Subject Kimberly Akimbo E773288 entity
Predicate originalBroadwayCastRole P4737 FINISHED
Object Kimberly Levaco
Kimberly Levaco is the teenage protagonist of the musical "Kimberly Akimbo," a girl with a rare aging condition navigating family chaos and adolescent challenges.
 E1914423 NE FINISHED

How this triple was built (3 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: Kimberly Levaco | Statement: [Kimberly Akimbo, originalBroadwayCastRole, Kimberly Levaco]
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: Kimberly Levaco
Triple: [Kimberly Akimbo, originalBroadwayCastRole, Kimberly Levaco]
Generated description
Kimberly Levaco is the teenage protagonist of the musical "Kimberly Akimbo," a girl with a rare aging condition navigating family chaos and adolescent challenges.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: originalBroadwayCastRole
Context triple: [Kimberly Akimbo, originalBroadwayCastRole, Kimberly Levaco]
  • A. originalBroadwayCoStar
    Indicates that two performers appeared together as co-stars in the original Broadway production of the same show.
  • B. portrayedInBroadwayProductionBy
    Indicates that an entity was depicted or performed in a Broadway stage production by a specified person or group.
  • C. originalBroadwayStar chosen
    Indicates that the subject was a member of the original Broadway cast in the specified role or production.
  • D. appearedInBroadwayProduction
    Indicates that an entity participated as part of a Broadway stage production of another work or show.
  • E. originalBroadwayDaveyActor
    Indicates that the subject is the actor who played the role of Davey in the original Broadway production.
  • F. None of above.

Provenance (6 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_69f2248facd48190b183c3f3ca6daef7 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_6a00383e868c819098fd17e25fcbdb04 completed May 10, 2026, 7:48 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2798ab397881908de829925172f893 completed June 9, 2026, 4:38 a.m.
NEDg Description generation batch_6a279a025d0481909e5d9eea25f94b47 completed June 9, 2026, 4:43 a.m.
NED2 Entity disambiguation (via description) batch_6a279aaa7f48819093ec1b953b8d9792 completed June 9, 2026, 4:46 a.m.
PD Predicate disambiguation batch_6a0037cc59688190b7b9da939a413db3 completed May 10, 2026, 7:46 a.m.
Created at: April 29, 2026, 8:03 p.m.