Triple
T380358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lisbon Strategy |
E8663
|
entity |
| Predicate | setsTarget |
P4492
|
FINISHED |
| Object | higher employment rates in the EU |
—
|
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: higher employment rates in the EU | Statement: [Lisbon Strategy, setsTarget, higher employment rates in the EU]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: setsTarget Context triple: [Lisbon Strategy, setsTarget, higher employment rates in the EU]
-
A.
setsOut
Indicates that an entity begins a journey, course of action, or process, moving from an initial state or location toward a goal or destination.
-
B.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
-
C.
setting
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
D.
sets
chosen
Indicates that an entity places, positions, or puts another entity into a particular state, location, or configuration.
-
E.
targetScoreRule
Indicates the rule or criteria used to determine the target score to be achieved or applied in a given context.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec2c95088190a603bb1ee076ebd6 |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e964d4b481909290e474b0341e3c |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.