Triple
T17282921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 36th District Court |
E419577
|
entity |
| Predicate | hasCaseloadCharacteristic |
P99469
|
FINISHED |
| Object | high-volume court |
—
|
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: high-volume court | Statement: [36th District Court, hasCaseloadCharacteristic, high-volume court]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaseloadCharacteristic Context triple: [36th District Court, hasCaseloadCharacteristic, high-volume court]
-
A.
hasJurisdictionCharacteristic
Indicates that a jurisdiction possesses a particular legal, regulatory, or administrative characteristic.
-
B.
hasNumberOfCasesApprox
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
C.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
D.
subjectHasCharacteristic
chosen
Indicates that a subject possesses, exhibits, or is defined by a particular characteristic or attribute.
-
E.
hasCourseCharacteristic
Indicates that a course possesses or is associated with a particular characteristic, feature, or attribute.
- 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_69d886da626481908a14ce7830329a35 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e4332b19f481908acfa88b2f57c5dc |
completed | April 19, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69e3832c3b98819091967ac7e91ba316 |
completed | April 18, 2026, 1:12 p.m. |
Created at: April 10, 2026, 5:40 a.m.