Triple

T10102000
Position Surface form Disambiguated ID Type / Status
Subject Chowder E216224 entity
Predicate setting P1957 FINISHED
Object Marzipan City
Marzipan City is the whimsical, food-themed fictional town where the animated series "Chowder" takes place.
 E841258 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: Marzipan City | Statement: [Chowder, setting, Marzipan City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marzipan City
Context triple: [Chowder, setting, Marzipan City]
  • A. Bogo City
    Bogo City is a component city in the northern part of Cebu province in the Philippines, known as a commercial and transport hub for surrounding rural municipalities.
  • B. Gup City
    Gup City is a fantastical, talkative metropolis of light and open debate in Salman Rushdie’s novel "Haroun and the Sea of Stories."
  • C. Syrup City
    Syrup City is the nickname of Cairo, a small city in southern Georgia known historically for its syrup production and related agricultural industry.
  • D. Coolville
    Coolville is a small village in southeastern Ohio, United States, known for its rural setting near the Hocking and Ohio rivers.
  • E. Chocolate City
    Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
  • 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: Marzipan City
Triple: [Chowder, setting, Marzipan City]
Generated description
Marzipan City is the whimsical, food-themed fictional town where the animated series "Chowder" takes place.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marzipan City
Target entity description: Marzipan City is the whimsical, food-themed fictional town where the animated series "Chowder" takes place.
  • A. Bogo City
    Bogo City is a component city in the northern part of Cebu province in the Philippines, known as a commercial and transport hub for surrounding rural municipalities.
  • B. Gup City
    Gup City is a fantastical, talkative metropolis of light and open debate in Salman Rushdie’s novel "Haroun and the Sea of Stories."
  • C. Syrup City
    Syrup City is the nickname of Cairo, a small city in southern Georgia known historically for its syrup production and related agricultural industry.
  • D. Coolville
    Coolville is a small village in southeastern Ohio, United States, known for its rural setting near the Hocking and Ohio rivers.
  • E. Chocolate City
    Chocolate City is a popular nickname for Washington, D.C., highlighting its historically large and influential African American population and culture.
  • 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_69ca83d039f08190b9d10363221c69fb completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cdd099c21c819097aac4f0f168a2da completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6d3efec8190b1432ca614aeb334 completed April 5, 2026, 7:24 p.m.
NEDg Description generation batch_69d2b7aecdb081909f651c1bc1bcfd75 completed April 5, 2026, 7:27 p.m.
NED2 Entity disambiguation (via description) batch_69d2b86bf8948190a79046efadc4adea completed April 5, 2026, 7:30 p.m.
Created at: March 30, 2026, 9:02 p.m.