Triple

T3571374
Position Surface form Disambiguated ID Type / Status
Subject Worlds of Fun E75577 entity
Predicate hasRollerCoaster P23566 FINISHED
Object Mamba
Mamba is a high-speed steel hypercoaster at Worlds of Fun in Kansas City, Missouri, known for its large drops and airtime-filled layout.
 E368789 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: Mamba | Statement: [Worlds of Fun, hasRollerCoaster, Mamba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mamba
Context triple: [Worlds of Fun, hasRollerCoaster, Mamba]
  • A. White Mamba
    White Mamba is the nickname of Diana Taurasi, a legendary WNBA guard renowned for her scoring ability, competitiveness, and clutch performances.
  • B. Motobu
    Motobu is a town on the northern part of Okinawa Island in Japan, known for its coastal scenery, marine attractions, and role as a regional tourist destination.
  • C. Diego
    Diego is a given name of Spanish origin commonly used in Spanish-speaking countries and beyond.
  • D. Mako
    Mako was a Japanese-American actor and voice actor known for his distinctive voice and roles in films like "Conan the Barbarian" and as the voice of Iroh in "Avatar: The Last Airbender."
  • E. Mako
    Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
  • 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: Mamba
Triple: [Worlds of Fun, hasRollerCoaster, Mamba]
Generated description
Mamba is a high-speed steel hypercoaster at Worlds of Fun in Kansas City, Missouri, known for its large drops and airtime-filled layout.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mamba
Target entity description: Mamba is a high-speed steel hypercoaster at Worlds of Fun in Kansas City, Missouri, known for its large drops and airtime-filled layout.
  • A. White Mamba
    White Mamba is the nickname of Diana Taurasi, a legendary WNBA guard renowned for her scoring ability, competitiveness, and clutch performances.
  • B. Motobu
    Motobu is a town on the northern part of Okinawa Island in Japan, known for its coastal scenery, marine attractions, and role as a regional tourist destination.
  • C. Diego
    Diego is a given name of Spanish origin commonly used in Spanish-speaking countries and beyond.
  • D. Mako
    Mako was a Japanese-American actor and voice actor known for his distinctive voice and roles in films like "Conan the Barbarian" and as the voice of Iroh in "Avatar: The Last Airbender."
  • E. Mako
    Mako is a Japanese imperial family member best known as Princess Mako of Akishino, the former princess who left royal status upon her marriage to a commoner.
  • 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_69ad85d512708190829c8b2d3a2ccfb8 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc0c32624819097a96b3d62e3d8f0 completed March 8, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bbbcf2d08190901049948df66f0c completed March 13, 2026, 7:24 a.m.
NEDg Description generation batch_69b3bca07cac81908253b2b4225f3d67 completed March 13, 2026, 7:28 a.m.
NED2 Entity disambiguation (via description) batch_69b3f5b6e66c81908700d5f3df0a864d completed March 13, 2026, 11:32 a.m.
Created at: March 8, 2026, 3:21 p.m.