Triple

T575186
Position Surface form Disambiguated ID Type / Status
Subject C++ E13747 entity
Predicate influencedBy P9 FINISHED
Object Ada E8676 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: Ada | Statement: [C++, influencedBy, Ada]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ada
Context triple: [C++, influencedBy, Ada]
  • A. Ada (programming language) chosen
    Ada is a statically typed, high-level programming language designed with strong support for reliability, safety, and real-time systems, widely used in mission-critical and embedded applications such as aerospace and defense.
  • B. Julia
    Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
  • C. APL
    APL is a widely cited peer-reviewed scientific journal focusing on rapid publication of significant new research in applied physics.
  • D. ObjectAda
    ObjectAda is a commercial, object-oriented Ada development environment and compiler suite used for building and maintaining Ada applications.
  • E. Pascal
    Pascal is a high-level, strongly typed procedural programming language designed by Niklaus Wirth in the late 1960s, widely used for teaching structured programming and data structuring concepts.
  • 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_69a4933fa4d88190a7949cc83c08c5c1 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49b67395c8190a8046ff7debe9d1f completed March 1, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4ff4db0248190b2b3ca0290467313 completed March 2, 2026, 3:09 a.m.
Created at: March 1, 2026, 7:33 p.m.