Triple

T19107394
Position Surface form Disambiguated ID Type / Status
Subject Climate Change Act 2008 E467691 entity
Predicate original2050Target P860 FINISHED
Object at least 80% reduction from 1990 levels 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: at least 80% reduction from 1990 levels | Statement: [Climate Change Act 2008, original2050Target, at least 80% reduction from 1990 levels]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: original2050Target
Context triple: [Climate Change Act 2008, original2050Target, at least 80% reduction from 1990 levels]
  • A. plannedTarget
    Indicates that one entity has been designated or selected as the intended target or objective of another entity’s planned action or operation.
  • B. target chosen
    Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
  • C. primaryTarget
    Indicates that an entity is the main or most important target of another entity’s action, focus, or effect.
  • D. conversionTarget
    Indicates that one entity serves as the intended outcome, goal, or result that another entity is meant to be converted or transformed into.
  • E. optimizationTarget
    Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
  • 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_69d8dd06a26481908039e2a1bae8c597 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e391245c8190b1393577b61c4f76 completed April 20, 2026, 8:28 a.m.
PD Predicate disambiguation batch_69e4b9ac41848190afd0f33b42cebe99 completed April 19, 2026, 11:17 a.m.
Created at: April 10, 2026, 12:04 p.m.