Triple

T14343370
Position Surface form Disambiguated ID Type / Status
Subject ScalaTest E355655 entity
Predicate supportsTestingStyle P93450 FINISHED
Object PropSpec
PropSpec is a ScalaTest testing style that facilitates property-based testing by allowing developers to define properties that should hold true for a range of generated inputs.
 E1094381 NE FINISHED

How this triple was built (4 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: PropSpec | Statement: [ScalaTest, supportsTestingStyle, PropSpec]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PropSpec
Context triple: [ScalaTest, supportsTestingStyle, PropSpec]
  • A. ParamSpec
    ParamSpec is a Python typing construct that allows you to capture and reuse the parameter types of callable objects, enabling more precise type annotations for higher-order functions.
  • B. PROP
    PROP is the acronym for the NCAA Playing Rules Oversight Panel, the body responsible for approving and overseeing playing rules across NCAA sports.
  • C. Specs
    Specs is a quirky paranormal investigator and comic-relief character in the Insidious horror film series.
  • D. A-Spec
    A-Spec is the primary single-player driving mode in the Gran Turismo series where players directly control cars in races and events.
  • E. A-Spec
    A-Spec is Acura’s sport-oriented trim and styling package that enhances select models with more aggressive design cues and performance-focused features.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PropSpec
Triple: [ScalaTest, supportsTestingStyle, PropSpec]
Generated description
PropSpec is a ScalaTest testing style that facilitates property-based testing by allowing developers to define properties that should hold true for a range of generated inputs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PropSpec
Target entity description: PropSpec is a ScalaTest testing style that facilitates property-based testing by allowing developers to define properties that should hold true for a range of generated inputs.
  • A. ParamSpec
    ParamSpec is a Python typing construct that allows you to capture and reuse the parameter types of callable objects, enabling more precise type annotations for higher-order functions.
  • B. PROP
    PROP is the acronym for the NCAA Playing Rules Oversight Panel, the body responsible for approving and overseeing playing rules across NCAA sports.
  • C. Specs
    Specs is a quirky paranormal investigator and comic-relief character in the Insidious horror film series.
  • D. A-Spec
    A-Spec is the primary single-player driving mode in the Gran Turismo series where players directly control cars in races and events.
  • E. A-Spec
    A-Spec is Acura’s sport-oriented trim and styling package that enhances select models with more aggressive design cues and performance-focused features.
  • F. None of above. chosen

Provenance (5 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_69d82790a7e08190877e2d349b2e8d8e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de8e89ed9c8190acdb647ee618e919 completed April 14, 2026, 6:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd469d899081909103563f209dd944 completed May 8, 2026, 2:12 a.m.
NEDg Description generation batch_69fd47fa764c8190b1d691f5847b7a05 completed May 8, 2026, 2:18 a.m.
NED2 Entity disambiguation (via description) batch_69fd492226888190a014b23e506ab19c completed May 8, 2026, 2:23 a.m.
Created at: April 10, 2026, 1:14 a.m.