Triple

T1038025
Position Surface form Disambiguated ID Type / Status
Subject Lieutenant Governor of Guam E22408 entity
Predicate isModeledAfter P7125 FINISHED
Object state lieutenant governor offices in the United States 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: state lieutenant governor offices in the United States | Statement: [Lieutenant Governor of Guam, isModeledAfter, state lieutenant governor offices in the United States]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: isModeledAfter
Context triple: [Lieutenant Governor of Guam, isModeledAfter, state lieutenant governor offices in the United States]
  • A. hasModelledFor
    Indicates that one entity has served as a model for another entity, typically in a professional or representational context such as art, photography, or fashion.
  • B. isVersionOf
    Indicates that one entity is a particular version, edition, or variant derived from another entity.
  • C. isBasedOn chosen
    Indicates that one entity is derived from, inspired by, or developed using the content, structure, or principles of another entity.
  • D. isDesignedAs
    Indicates that something has been intentionally created or configured to serve as or function in the role of something else.
  • E. model
    Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
  • 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_69a493d91478819094cc01fb65564bc1 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b97c64a88190bf1119fdd4940bf3 completed March 1, 2026, 10:11 p.m.
PD Predicate disambiguation batch_69a4b729f8488190b2042bd9c625a833 completed March 1, 2026, 10:01 p.m.
Created at: March 1, 2026, 7:41 p.m.