Triple

T12183812
Position Surface form Disambiguated ID Type / Status
Subject Crash Course E290282 entity
Predicate hasPart P35 FINISHED
Object Crash Course Computer Science E290282 NE FINISHED

How this triple was built (2 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: Crash Course Computer Science | Statement: [Crash Course, hasPart, Crash Course Computer Science]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Crash Course Computer Science
Context triple: [Crash Course, hasPart, Crash Course Computer Science]
  • A. Great Principles of Computing
    Great Principles of Computing is a foundational book that articulates the core concepts, theories, and enduring ideas that underlie the field of computer science.
  • B. CS100
    CS100 is the original Bombardier CSeries narrow-body jet model that was later rebranded as the Airbus A220-100.
  • C. Crash Course chosen
    Crash Course is an educational YouTube series that offers fast-paced, engaging video lessons on a wide range of academic subjects.
  • D. The Science of Computing
    "The Science of Computing" is a foundational work by Peter J. Denning that explores the principles, theory, and practice underlying computer science as a scientific discipline.
  • E. MIT 6.001
    MIT 6.001 was a foundational introductory computer science course at MIT that emphasized abstraction, recursion, and programming language design, famously taught using the Scheme language.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab64de5881908d56eb7a75c6cc69 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d915fd8dac8190928059ad2b6bbbf3 completed April 10, 2026, 3:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f6aecb0881909084f3ff2a9e52ea completed May 2, 2026, 1:05 p.m.
Created at: April 8, 2026, 9:50 p.m.