Triple
T36421788
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Traffic Court of Jamaica |
E897176
|
entity |
| Predicate | caseTypeExample |
P67300
|
FINISHED |
| Object | speeding offences |
—
|
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: speeding offences | Statement: [Traffic Court of Jamaica, caseTypeExample, speeding offences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseTypeExample Context triple: [Traffic Court of Jamaica, caseTypeExample, speeding offences]
-
A.
caseTypes
Indicates the types or categories of cases associated with or applicable to an entity or situation.
-
B.
exampleType
chosen
Indicates that one entity serves as a representative or illustrative instance of the type or category defined by another entity.
-
C.
typicalCaseTypes
Indicates the kinds or categories of cases that are most commonly associated with or handled by a given entity.
-
D.
usesCase
Indicates that one entity employs, applies, or makes practical use of another entity for a particular purpose or function.
-
E.
caseTypeSpecialization
Indicates that one case type is a more specific or specialized subtype of another, more general case type.
- 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_69f76e559b10819099d6655a6e14587c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bcccd7988190aa5c931ff347d33c |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:10 p.m.