Triple
T34234617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Local Court of New South Wales |
E878298
|
entity |
| Predicate | hasNumberOfCourthouses |
P7595
|
FINISHED |
| Object | more than 100 locations across New South Wales |
—
|
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: more than 100 locations across New South Wales | Statement: [Local Court of New South Wales, hasNumberOfCourthouses, more than 100 locations across New South Wales]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCourthouses Context triple: [Local Court of New South Wales, hasNumberOfCourthouses, more than 100 locations across New South Wales]
-
A.
numberOfCourts
Indicates the quantity of courts associated with or present at a given entity or location.
-
B.
hasCourthouse
chosen
Indicates that a location or jurisdiction possesses or contains a courthouse.
-
C.
hasCourts
Indicates that an entity possesses, contains, or is equipped with one or more courts (e.g., legal, sports, or judicial facilities).
-
D.
numberOfDistrictCourts
Indicates the total count of district courts associated with a given entity.
-
E.
numberOfCourtrooms
Indicates the total count of courtrooms associated with a given legal facility, jurisdiction, or court 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_69f349b22d8c819096b22df268382aa9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fe91383a1c81909266e40c3c3ede6c |
completed | May 9, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69fe8fde094081908f0f121664fbb5c7 |
completed | May 9, 2026, 1:37 a.m. |
Created at: May 1, 2026, 1:56 a.m.