Triple

T30084831
Position Surface form Disambiguated ID Type / Status
Subject Schumpeterian growth theory E764574 entity
Predicate modelFeature P104084 FINISHED
Object R&D sector producing innovations 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: R&D sector producing innovations | Statement: [Schumpeterian growth theory, modelFeature, R&D sector producing innovations]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: modelFeature
Context triple: [Schumpeterian growth theory, modelFeature, R&D sector producing innovations]
  • A. featuresModel chosen
    Indicates that one entity includes, exposes, or is characterized by a particular model as one of its defining components or capabilities.
  • B. featureSet
    Indicates that one entity is a collection or configuration of features associated with or applied to another entity.
  • C. featuresMode
    Indicates that one entity operates in, supports, or is characterized by a particular mode or configuration specified by another entity.
  • D. equipmentCharacteristic
    Indicates that a specific characteristic, property, or attribute is associated with a piece of equipment.
  • E. targetFeature
    Indicates that one entity is the specific feature, attribute, or characteristic that another entity is directed toward, focused on, or intended to affect.
  • 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_69f22473c0fc8190a926a8051b3b378b completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67d6b85488190a14499b6bb2c9241 completed May 2, 2026, 10:40 p.m.
PD Predicate disambiguation batch_69f675ff62c48190a634bbb8896973b9 completed May 2, 2026, 10:09 p.m.
Created at: April 29, 2026, 7:03 p.m.