Triple
T20611911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Orley Farm |
E506466
|
entity |
| Predicate | hasLegalCaseType |
P4217
|
FINISHED |
| Object | forgery allegation |
—
|
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: forgery allegation | Statement: [Orley Farm, hasLegalCaseType, forgery allegation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalCaseType Context triple: [Orley Farm, hasLegalCaseType, forgery allegation]
-
A.
hasTypeOfCase
chosen
Indicates that an entity is associated with or classified under a particular type or category of case.
-
B.
hasLegalSystemType
Indicates that an entity possesses or is governed by a particular type or form of legal system.
-
C.
hasTypeOfCourt
Indicates that an entity is associated with or classified by a specific type or category of court.
-
D.
legalTestType
Indicates the specific kind or category of legal test or standard that is applied in a given legal context or proceeding.
-
E.
hasLegalIssue
Indicates that an entity is involved in, associated with, or subject to a legal problem, dispute, or proceeding.
- 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_69e0b4bb2b4081908fa4a72444120f35 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aad81bdc8190aa6f6164f406a468 |
completed | April 20, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69e5a00c43308190b7ea58d559257e07 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:41 a.m.