Triple

T583683
Position Surface form Disambiguated ID Type / Status
Subject Rutte II cabinet E15110 entity
Predicate budgetaryPolicy P11306 FINISHED
Object austerity measures 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: austerity measures | Statement: [Rutte II cabinet, budgetaryPolicy, austerity measures]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: budgetaryPolicy
Context triple: [Rutte II cabinet, budgetaryPolicy, austerity measures]
  • A. budgetaryEffect
    Indicates the financial impact or change in budget resulting from a particular action, decision, or policy.
  • B. budgetFunction
    Indicates a relationship where a specific budgeting rule or formula determines how resources or funds are allocated or constrained for an entity or activity.
  • C. economicPolicyType chosen
    Indicates the classification of an economic policy according to its general type or category (e.g., fiscal, monetary, trade).
  • D. budget
    Indicates that an entity allocates, plans, or assigns specific financial resources for another entity, activity, or time period.
  • E. budgetAuthority
    Indicates that an entity has the formal power or authorization to allocate, commit, or control the use of financial resources for another entity or activity.
  • 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_69a4935783b8819082b77726ec10cc42 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49b8745c88190af9672e5fe8396c3 completed March 1, 2026, 8:03 p.m.
PD Predicate disambiguation batch_69a494c9315c8190a773e8e00737d8a0 completed March 1, 2026, 7:34 p.m.
Created at: March 1, 2026, 7:33 p.m.