Triple

T1465759
Position Surface form Disambiguated ID Type / Status
Subject Robert B. Leighton E27017 entity
Predicate employerRole P13957 FINISHED
Object professor of physics at the California Institute of Technology — 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: professor of physics at the California Institute of Technology | Statement: [Robert B. Leighton, employerRole, professor of physics at the California Institute of Technology]
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: employerRole
Context triple: [Robert B. Leighton, employerRole, professor of physics at the California Institute of Technology]
  • A. employmentType
    Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
  • B. employerType
    Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
  • C. urbanRole
    Indicates the function, status, or role that an entity holds within an urban or city context.
  • D. employer
    Indicates a relationship where one entity hires, pays, and oversees the work of another entity.
  • E. hasOrganizationalRole chosen
    Indicates that an entity holds a specific role, position, or function within an organization.
  • 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_69a496d25d6881909dbd84f86d763992 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c5bb4e288190997c7e8985e9a2bd completed March 1, 2026, 11:03 p.m.
PD Predicate disambiguation batch_69a4c48121e48190946c23c583e5fb64 completed March 1, 2026, 10:58 p.m.
Created at: March 1, 2026, 8:01 p.m.