Triple
T13623222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NFL Rooney Rule |
E325510
|
entity |
| Predicate | hasEnforcement |
P75822
|
FINISHED |
| Object | potential fines for noncompliance |
—
|
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: potential fines for noncompliance | Statement: [NFL Rooney Rule, hasEnforcement, potential fines for noncompliance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEnforcement Context triple: [NFL Rooney Rule, hasEnforcement, potential fines for noncompliance]
-
A.
canEnforce
Indicates that one entity has the authority or capability to compel compliance with rules, decisions, or obligations upon another entity.
-
B.
enforcedOn
Indicates that a rule, policy, or constraint is applied with authority to a particular target or subject.
-
C.
alsoEnforcedBy
Indicates that the same rule, policy, or constraint is enforced by an additional authority, mechanism, or entity beyond the primary one.
-
D.
licenseEnforced
Indicates that a license’s terms or restrictions are actively applied and upheld in relation to the associated entity or activity.
-
E.
enforcementStrength
chosen
Indicates the degree or intensity with which rules, laws, or policies are applied and enforced in a given 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc60635d08190899806fe8936f02a |
completed | April 12, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69dbbe85e1c4819095194f4b7f9f6118 |
completed | April 12, 2026, 3:47 p.m. |
Created at: April 9, 2026, 9:50 p.m.