Triple
T8753223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janus v. AFSCME |
E208010
|
entity |
| Predicate | affectedAreaOfLaw |
P2167
|
FINISHED |
| Object | public-sector labor law |
—
|
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: public-sector labor law | Statement: [Janus v. AFSCME, affectedAreaOfLaw, public-sector labor law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectedAreaOfLaw Context triple: [Janus v. AFSCME, affectedAreaOfLaw, public-sector labor law]
-
A.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
B.
legalArea
chosen
Indicates the specific field or branch of law that a legal matter, case, or document pertains to.
-
C.
affectedJurisdictionOver
Indicates that one entity’s authority, control, or legal power extends over and impacts the jurisdiction of another entity.
-
D.
branchOfLaw
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
E.
legalMatters
Indicates that one entity is involved with, concerned about, or responsible for legal issues, processes, or obligations related to another entity or context.
- 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_69ca835cd6b08190bd7c63db92f53c86 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5dd714dc8190bccc4d52f988958d |
completed | March 31, 2026, 11:50 p.m. |
| PD | Predicate disambiguation | batch_69cc5c160dac8190b4aeb4bf0529de52 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:39 p.m.