Triple
T10667066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fit for 55 package |
E251382
|
entity |
| Predicate | targetReduction |
P95224
|
FINISHED |
| Object | at least 55 percent net greenhouse gas emissions reduction |
—
|
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 55 percent net greenhouse gas emissions reduction | Statement: [Fit for 55 package, targetReduction, at least 55 percent net greenhouse gas emissions reduction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetReduction Context triple: [Fit for 55 package, targetReduction, at least 55 percent net greenhouse gas emissions reduction]
-
A.
errorReductionGoal
Indicates that an entity has a target or objective to decrease the number or rate of errors relative to a current or baseline level.
-
B.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
C.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of another entity.
-
D.
noiseReductionGoal
Indicates the intended target level or objective for reducing noise in a given context or system.
-
E.
targetInflationRate
Indicates the inflation level that a monetary authority aims to achieve and maintain over a specified period.
- F. None of above. chosen
Provenance (4 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6f3204bac8190b9bd8bfcc705b06b |
completed | April 9, 2026, 12:30 a.m. |
| PD | Predicate disambiguation | batch_69d6dd8a93208190a573061387e2aebb |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df47899481909ac0e518d94883cb |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:08 p.m.