Triple

T8689962
Position Surface form Disambiguated ID Type / Status
Subject Joe Morton E206260 entity
Predicate notableWork P4 FINISHED
Object Eureka
Eureka is a science fiction television series centered on a secret town of brilliant scientists whose experiments frequently lead to unintended and often dangerous consequences.
E331099 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: Eureka | Statement: [Joe Morton, notableWork, Eureka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eureka
Context triple: [Joe Morton, notableWork, Eureka]
  • A. Eureka
    Eureka is a historic coastal city in Northern California known for its Victorian architecture, maritime heritage, and role as a regional cultural and economic center.
  • B. Eureka
    Eureka is the codename for the Eureka Conference, a World War II meeting between Allied leaders Franklin D. Roosevelt, Winston Churchill, and Joseph Stalin held in Tehran in 1943.
  • C. Eureka
    Eureka is a historic side-wheel paddle steamboat and former ferry now preserved as a museum ship in San Francisco.
  • D. Eureka
    Eureka is a small city in central Illinois best known as the home of Eureka College, where Ronald Reagan studied.
  • E. Eureka
    Eureka is a historic mining town in eastern Nevada known for its well-preserved 19th-century architecture and role in the region’s silver boom.
  • 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: Eureka
Triple: [Joe Morton, notableWork, Eureka]
Generated description
Eureka is a science fiction television series centered on a secret town of brilliant scientists whose experiments frequently lead to unintended and often dangerous consequences.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Eureka
Target entity description: Eureka is a science fiction television series centered on a secret town of brilliant scientists whose experiments frequently lead to unintended and often dangerous consequences.
  • A. Eureka chosen
    Eureka is a science fiction television series that follows the quirky residents of a secret high-tech town where brilliant scientists’ experiments frequently go awry.
  • B. Eureka
    Eureka is a historic mining town in eastern Nevada known for its well-preserved 19th-century architecture and role in the region’s silver boom.
  • C. Eureka
    Eureka is a small, remote research and weather station in the Canadian High Arctic, known as one of the northernmost permanently inhabited places in the world.
  • D. Eureka
    Eureka is a historic coastal city in Northern California known for its Victorian architecture, maritime heritage, and role as a regional cultural and economic center.
  • E. Eureka
    Eureka is a historic side-wheel paddle steamboat and former ferry now preserved as a museum ship in San Francisco.
  • 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_69ca835481fc819084e33d3bc883bfa6 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5734602c81909a0687e00f4a4a26 completed March 31, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cef3df73b88190b67138ee5129de8b completed April 2, 2026, 10:55 p.m.
NEDg Description generation batch_69cef52200788190a8173da1aaa4f681 completed April 2, 2026, 11 p.m.
NED2 Entity disambiguation (via description) batch_69cef6c109b08190bb29ce2747f3ccb7 completed April 2, 2026, 11:07 p.m.
Created at: March 30, 2026, 6:33 p.m.