Triple
T34292466
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elite |
E879927
|
entity |
| Predicate | mediaTypeSpecialization |
P182286
|
FINISHED |
| Object | scripted television |
—
|
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: scripted television | Statement: [Elite, mediaTypeSpecialization, scripted television]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaTypeSpecialization Context triple: [Elite, mediaTypeSpecialization, scripted television]
-
A.
mediaType
Indicates the format or category of media associated with an entity, such as text, image, audio, or video.
-
B.
mediaTypeOrigin
Indicates the original source or context from which a particular media type was derived or introduced.
-
C.
mediaTypeAssociation
Indicates an association between an entity and a specific type or category of media with which it is related or represented.
-
D.
ownedMediaType
Indicates the type or category of media content that is owned in the context of the ownership relationship.
-
E.
mediaTypeExample
Indicates that something serves as an example or illustrative instance of a particular media type.
- F. None of above. chosen
Provenance (4 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_69f349b6df1c81908e5e5b6c2ab6409b |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7886be6d8819095ec62e4f2cee858 |
completed | May 3, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69f7841440f48190b4346c08855951d2 |
completed | May 3, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69f7886b27f08190ab4580f949222c93 |
completed | May 3, 2026, 5:39 p.m. |
Created at: May 1, 2026, 1:57 a.m.