Triple

T9135512
Position Surface form Disambiguated ID Type / Status
Subject Cedar Point E219191 entity
Predicate hasRollerCoaster P23566 FINISHED
Object Maverick
Maverick is a high-speed steel roller coaster at Cedar Point known for its intense launches, sharp turns, and low-to-the-ground layout.
 E492909 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: Maverick | Statement: [Cedar Point, hasRollerCoaster, Maverick]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maverick
Context triple: [Cedar Point, hasRollerCoaster, Maverick]
  • A. Maverick
    Maverick is an MBTA subway station on Boston’s Blue Line serving the East Boston neighborhood.
  • B. Maverick
    Maverick is a 1994 comedic Western film starring Mel Gibson, Jodie Foster, and James Garner, centered on a charming gambler trying to raise money for a high-stakes poker tournament.
  • C. Maverick
    Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
  • D. Maverick
    Maverick is a classic American Western comedy television series that aired in the late 1950s, following the adventures of charming, poker-playing gambler Bret Maverick and his relatives.
  • E. Maverick
    Maverick is a political nickname for U.S. Senator John McCain, reflecting his reputation for independence and willingness to break with his party.
  • 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: Maverick
Triple: [Cedar Point, hasRollerCoaster, Maverick]
Generated description
Maverick is a high-speed steel roller coaster at Cedar Point known for its intense launches, sharp turns, and low-to-the-ground layout.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maverick
Target entity description: Maverick is a high-speed steel roller coaster at Cedar Point known for its intense launches, sharp turns, and low-to-the-ground layout.
  • A. Maverick chosen
    Maverick is a high-speed steel roller coaster at Cedar Point in Ohio, renowned for its intense launches, inversions, and twisted track layout.
  • B. Maverick
    Maverick is an MBTA subway station on Boston’s Blue Line serving the East Boston neighborhood.
  • C. Maverick
    Maverick is a cigarette brand known for its budget-friendly positioning within the U.S. tobacco market.
  • D. Maverick
    Maverick is the daring U.S. Navy fighter pilot Pete "Maverick" Mitchell, the iconic lead character of the Top Gun film series.
  • E. Maverick
    Maverick is a 1994 comedic Western film starring Mel Gibson, Jodie Foster, and James Garner, centered on a charming gambler trying to raise money for a high-stakes poker tournament.
  • 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_69ca83e012288190a5771058adbaabd2 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca8df68108190bb2df0583c5ad481 completed April 1, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d047efc5e48190bc8c4a7e865faef9 completed April 3, 2026, 11:06 p.m.
NEDg Description generation batch_69d04935d4e88190acb4d65a2dc2bc8a completed April 3, 2026, 11:11 p.m.
NED2 Entity disambiguation (via description) batch_69d049e6c4cc81909e08b5aaed9a88dc completed April 3, 2026, 11:14 p.m.
Created at: March 30, 2026, 7:18 p.m.