Triple
T109389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SnagFilms |
E2210
|
entity |
| Predicate | mediaFormat |
P131
|
FINISHED |
| Object | digital streaming |
—
|
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: digital streaming | Statement: [SnagFilms, mediaFormat, digital streaming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mediaFormat Context triple: [SnagFilms, mediaFormat, digital streaming]
-
A.
mediaType
chosen
Indicates the format or category of media associated with an entity, such as text, image, audio, or video.
-
B.
format
Indicates the specific arrangement, structure, or presentation style in which something is organized or expressed.
-
C.
majorBrand
Indicates that the subject is a primary, widely recognized, or leading brand within its market or category in relation to the object.
-
D.
fareMedia
Indicates that a particular type of ticket, pass, or payment instrument is used as the medium for paying a fare.
-
E.
musicVideoFeatures
Indicates that a music video includes or prominently showcases a particular person, group, or element.
- 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_69a24fcdaeb48190a2d796677e4b3281 |
completed | Feb. 28, 2026, 2:15 a.m. |
| NER | Named-entity recognition | batch_69a25711f6788190a22252ea3a3af394 |
completed | Feb. 28, 2026, 2:46 a.m. |
| PD | Predicate disambiguation | batch_69a2563fd2fc819090265edbfe3092d6 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:20 a.m.