Triple
T9419678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Court of Chancery |
E227119
|
entity |
| Predicate | hadRemedyType |
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: [New York Court of Chancery, hadRemedyType, equitable remedies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadRemedyType Context triple: [New York Court of Chancery, hadRemedyType, 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.
typeOfRemedy
chosen
Indicates that one entity is a specific kind or category of remedy in relation to another entity.
-
C.
remedy
Indicates that one entity serves to cure, alleviate, or counteract a problem, illness, or undesirable condition affecting another entity.
-
D.
curedWith
Indicates that one entity is treated or healed by using another entity as the remedy or therapeutic method.
-
E.
hasCommonTreatment
Indicates that two or more entities share at least one treatment method or therapeutic approach in common.
- 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_69ca84359e7c819091148ba4b670e436 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd6c23c65081908d1009c26be66533 |
completed | April 1, 2026, 7:04 p.m. |
| PD | Predicate disambiguation | batch_69cca550777c819094e1851a6127cbbc |
completed | April 1, 2026, 4:55 a.m. |
Created at: March 30, 2026, 7:48 p.m.