Triple
T102305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Football League |
E2064
|
entity |
| Predicate | revenueModel |
P59
|
FINISHED |
| Object | national television contracts |
—
|
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: national television contracts | Statement: [National Football League, revenueModel, national television contracts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: revenueModel Context triple: [National Football League, revenueModel, national television contracts]
-
A.
revenueLevel
Indicates the relative amount or tier of revenue associated with an entity or activity.
-
B.
fundingModel
chosen
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
-
C.
model
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
D.
notableModel
Indicates that an entity is a particularly important, influential, or exemplary instance or version within a broader category or system.
-
E.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a258e0b11c8190b7b5cf3c354c47ce |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2563a6ff48190bec582fb2f99b7af |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.