Triple
T33299670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States District Court for the District of Minnesota |
E852543
|
entity |
| Predicate | numberOfJudgesAssigned |
P2279
|
FINISHED |
| Object | 7 |
—
|
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: 7 | Statement: [United States District Court for the District of Minnesota, numberOfJudgesAssigned, 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfJudgesAssigned Context triple: [United States District Court for the District of Minnesota, numberOfJudgesAssigned, 7]
-
A.
numberOfJudges
chosen
Indicates the total count of judges associated with a particular case, event, or entity.
-
B.
numberOfActiveJudgesAuthorized
Indicates the total count of judges who are officially permitted and currently authorized to serve in an active judicial capacity.
-
C.
hasJudges
Indicates that one entity serves as a judge or panel of judges for another entity, such as an event, competition, or legal case.
-
D.
numberOfCircuitJudgesCreated
Indicates the total count of circuit judge positions that have been established or created.
-
E.
numberOfPermanentJudges
Indicates the total count of judges who hold permanent (non-temporary) positions within a given judicial body or court.
- 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_69f34966ed4c81908dc9dda82d8c7fe3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a030c7d03308190a102689db1e8b657 |
completed | May 12, 2026, 11:18 a.m. |
| PD | Predicate disambiguation | batch_6a030a57b16c81909191270f64a77fcf |
completed | May 12, 2026, 11:09 a.m. |
Created at: May 1, 2026, 1:33 a.m.