Triple
T28089022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | federal appellate courts of Argentina |
E709903
|
entity |
| Predicate | remedyPower |
P164031
|
FINISHED |
| Object | revoke lower court decisions |
—
|
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: revoke lower court decisions | Statement: [federal appellate courts of Argentina, remedyPower, revoke lower court decisions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: remedyPower Context triple: [federal appellate courts of Argentina, remedyPower, revoke lower court decisions]
-
A.
remedyPower
chosen
Indicates that one entity has the capacity or effectiveness to cure, alleviate, or counteract the negative effects associated with another entity.
-
B.
remedy
Indicates that one entity serves to cure, alleviate, or counteract a problem, illness, or undesirable condition affecting another entity.
-
C.
hasRemedy
Indicates that one entity serves as a remedy, treatment, or corrective measure for a problem, condition, or undesirable state associated with another entity.
-
D.
typeOfRemedy
Indicates that one entity is a specific kind or category of remedy in relation to another entity.
-
E.
powerUp
Indicates an action where an entity increases or restores another entity’s energy, strength, or functional capacity.
- 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_69ef9b7037f0819095bb90eaccbcaf32 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641def1e88190a05bf865ced78b23 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 8:57 p.m.