Triple
T15916788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delaware Court of Chancery |
E385988
|
entity |
| Predicate | usesRemedies |
P13744
|
FINISHED |
| Object | equitable remedies |
—
|
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: equitable remedies | Statement: [Delaware Court of Chancery, usesRemedies, equitable remedies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesRemedies Context triple: [Delaware Court of Chancery, usesRemedies, equitable remedies]
-
A.
hasRemedy
Indicates that one entity serves as a remedy, treatment, or corrective measure for a problem, condition, or undesirable state associated with another entity.
-
B.
usesTreatment
Indicates that one entity applies or employs a particular treatment or therapeutic method on or for another entity.
-
C.
remedySought
Indicates that a particular legal or corrective action is being requested as a solution or relief in response to a problem or dispute.
-
D.
typeOfRemedy
chosen
Indicates that one entity is a specific kind or category of remedy in relation to another entity.
-
E.
remedy
Indicates that one entity serves to cure, alleviate, or counteract a problem, illness, or undesirable condition affecting another entity.
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:52 a.m.