Triple
T34982594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Radical Publishing |
E1008853
|
entity |
| Predicate | mediaTypeAdapted |
P131
|
FINISHED |
| Object | film |
—
|
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: film | Statement: [Radical Publishing, mediaTypeAdapted, film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaTypeAdapted Context triple: [Radical Publishing, mediaTypeAdapted, film]
-
A.
mediaTypeAdaptedFrom
Indicates that the media type of one resource is derived or adapted from the media type of another resource.
-
B.
mediaType
chosen
Indicates the format or category of media associated with an entity, such as text, image, audio, or video.
-
C.
mediaTypeSpecialization
Indicates a relationship where one media type is a more specific or specialized form of another, more general media type.
-
D.
mediaTypeAvailable
Indicates that a particular type or format of media is available for use, access, or distribution in the given context.
-
E.
mediaTypeExample
Indicates that something serves as an example or illustrative instance of a particular media type.
- 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_69f76dc844a48190881951fffb83d17e |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7b2f3a104819098ddd8909eaf596c |
completed | May 3, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69f7b1b8a9fc8190a1279e67a2d12707 |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:01 p.m.