Triple

T2059538
Position Surface form Disambiguated ID Type / Status
Subject FE Industrial and Systems E45754 entity
Predicate hasTargetDegree P6482 FINISHED
Object bachelor’s degree in industrial engineering 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: bachelor’s degree in industrial engineering | Statement: [FE Industrial and Systems, hasTargetDegree, bachelor’s degree in industrial engineering]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasTargetDegree
Context triple: [FE Industrial and Systems, hasTargetDegree, bachelor’s degree in industrial engineering]
  • A. hasDegree chosen
    Indicates that an entity possesses or has been awarded a specific academic or professional degree.
  • B. isDegreeOf
    Indicates that one entity is an academic or professional degree held, pursued, or associated with another entity.
  • C. hasTarget
    Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
  • D. hasDegreeOfFreedom
    Indicates that one entity possesses a specific independent parameter or mode in which it can vary or move relative to another entity or within a system.
  • E. typicalDegree
    Indicates the usual or characteristic level, intensity, or extent to which something holds or applies in a given context.
  • F. None of above.

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_69a8891a19508190a12ef1e192308dcb completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9af42988190a4e977154dd10312 completed March 7, 2026, 5:37 a.m.
PD Predicate disambiguation batch_69abb7ad5a7c8190b92575d6053b3fb7 completed March 7, 2026, 5:29 a.m.
Created at: March 4, 2026, 7:40 p.m.