Triple

T1382134
Position Surface form Disambiguated ID Type / Status
Subject Gaussian distribution E29361 entity
Predicate hasSkewness P27170 FINISHED
Object 0 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: 0 | Statement: [Gaussian distribution, hasSkewness, 0]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasSkewness
Context triple: [Gaussian distribution, hasSkewness, 0]
  • A. hasEccentricity
    Indicates that an object or orbit possesses a specific degree of deviation from being perfectly circular, quantified by its eccentricity value.
  • B. hasScale
    Indicates that one entity possesses or is characterized by a scale or graduated measurement system related to another entity.
  • C. hasCurvatureInvariant
    Indicates that one entity possesses a specific curvature-related invariant property or value associated with its geometric or mathematical structure.
  • D. hasOrientation
    Indicates that one entity is positioned or directed in a specific spatial or conceptual alignment relative to a reference frame or another entity.
  • E. isIsotropic
    Indicates that a property or behavior is identical in all directions, showing no directional dependence.
  • 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_69a498d883a48190bfdca525296ef7ee completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c3361bf08190b3f6bbf82e17685b completed March 1, 2026, 10:52 p.m.
PD Predicate disambiguation batch_69a4befe343c81909f758440a531b5be completed March 1, 2026, 10:34 p.m.
PDg Predicate description generation batch_69a4c0335f7081908d50046ced4cdee0 completed March 1, 2026, 10:39 p.m.
Created at: March 1, 2026, 7:59 p.m.