Triple
T13759016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munn v. Illinois |
E330549
|
entity |
| Predicate | typeOfRegulationUpheld |
P14058
|
FINISHED |
| Object | state regulation of maximum rates |
—
|
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: state regulation of maximum rates | Statement: [Munn v. Illinois, typeOfRegulationUpheld, state regulation of maximum rates]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRegulationUpheld Context triple: [Munn v. Illinois, typeOfRegulationUpheld, state regulation of maximum rates]
-
A.
upheldBy
Indicates that one entity is supported, maintained, or validated by another, often through approval, enforcement, or confirmation of its validity.
-
B.
regulationAtIssue
Indicates that a specific regulation is the subject of concern, dispute, or analysis in the given context.
-
C.
worksOnRegulationType
Indicates that an entity is involved in work or activities related to a specific type or category of regulation.
-
D.
supportsRegulation
Indicates that one entity endorses, backs, or advocates for the implementation or continuation of a specific regulation.
-
E.
regulatoryType
chosen
Indicates the specific kind or category of regulatory control, rule, or oversight that applies in the given relationship.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0223ab9081909db05334860405e0 |
completed | April 14, 2026, 9 a.m. |
| PD | Predicate disambiguation | batch_69dbbe97846c819093b00ea117b64e0d |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 10:09 p.m.