Triple

T3900671
Position Surface form Disambiguated ID Type / Status
Subject DDC E90479 entity
Predicate exampleClass P52756 FINISHED
Object 000 Computer science, information & general works LITERAL 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: 000 Computer science, information & general works | Statement: [DDC, exampleClass, 000 Computer science, information & general works]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: exampleClass
Context triple: [DDC, exampleClass, 000 Computer science, information & general works]
  • A. classIntroduced
    Indicates that a particular class (e.g., in a programming language or system) has been newly defined or made available within a given context or scope.
  • B. explorerClass
    Indicates that an entity belongs to, or is categorized within, a particular class or type of explorer.
  • C. codeExample
    Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
  • D. mainClasses
    Indicates that the subject represents the primary or most important classes associated with, defined within, or central to the object.
  • E. academyClass
    Indicates that an entity is a class, course, or instructional grouping offered within an academy or educational institution.
  • F. None of above. chosen

Provenance (4 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_69aed95d315881908cbf1bf4a7215fbf completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef1abe2dc81909c18aeae9b286898 completed March 9, 2026, 4:13 p.m.
PD Predicate disambiguation batch_69aee75b5b808190a348a31b1325d3d0 completed March 9, 2026, 3:29 p.m.
PDg Predicate description generation batch_69aef1aada308190821a3dfa6af170b3 completed March 9, 2026, 4:13 p.m.
Created at: March 9, 2026, 3:21 p.m.