Triple
T2323847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chancery Division |
E48240
|
entity |
| Predicate | hasCaseType |
P4217
|
FINISHED |
| Object | injunctive relief cases |
—
|
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: injunctive relief cases | Statement: [Chancery Division, hasCaseType, injunctive relief cases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaseType Context triple: [Chancery Division, hasCaseType, injunctive relief cases]
-
A.
hasTypeOfCase
chosen
Indicates that an entity is associated with or classified under a particular type or category of case.
-
B.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
C.
haveType
Indicates that an entity belongs to or is classified under a specified type or category.
-
D.
hasTypeOfCourt
Indicates that an entity is associated with or classified by a specific type or category of court.
-
E.
hasJudgeType
Indicates that an entity is associated with a specific category or type of judge.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc5909cc48190aab257313542dc49 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.