Triple
T3507051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NBA analyst for ESPN Radio |
E74100
|
entity |
| Predicate | typicalEmployerType |
P2510
|
FINISHED |
| Object | sports media network |
—
|
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: sports media network | Statement: [NBA analyst for ESPN Radio, typicalEmployerType, sports media network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEmployerType Context triple: [NBA analyst for ESPN Radio, typicalEmployerType, sports media network]
-
A.
typicalEmployer
Indicates that one entity is the kind of organization or person that commonly or usually employs the other entity.
-
B.
typicalEmployerUnit
Indicates that one entity is the standard or characteristic organizational unit that employs or is expected to employ another entity.
-
C.
employerType
chosen
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
D.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
E.
employmentType
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf52bd8819085a2ac5f48cc5c68 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.