Triple

T7789711
Position Surface form Disambiguated ID Type / Status
Subject Territoire de Belfort E187344 entity
Predicate regionReform P4888 FINISHED
Object Affected by 2016 French territorial reform 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: Affected by 2016 French territorial reform | Statement: [Territoire de Belfort, regionReform, Affected by 2016 French territorial reform]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: regionReform
Context triple: [Territoire de Belfort, regionReform, Affected by 2016 French territorial reform]
  • A. municipalityReform
    Indicates a formal reorganization or restructuring of a municipality’s administrative boundaries, governance, or status.
  • B. reform chosen
    Indicates bringing about significant changes to an existing system, practice, or entity in order to improve or correct it.
  • C. regionDivision
    Indicates a hierarchical or organizational subdivision relationship where one region is partitioned into smaller constituent regions.
  • D. capitalDivision
    Indicates that one administrative division serves as the capital of another administrative division or political entity.
  • E. reformsBy
    Indicates that one entity initiates, implements, or is responsible for changes or improvements (reforms) affecting another entity.
  • 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_69ca82af2d2c8190963861f5e0b8bf21 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cae7ea13f08190a60c5f1863bce816 completed March 30, 2026, 9:15 p.m.
PD Predicate disambiguation batch_69caa488532c819093ac40bba0b3c7ef completed March 30, 2026, 4:27 p.m.
Created at: March 30, 2026, 4:25 p.m.