Triple
T25535156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arlo cameras |
E640021
|
entity |
| Predicate | recordsVideoQuality |
P15682
|
FINISHED |
| Object | 1080p Full HD |
—
|
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: 1080p Full HD | Statement: [Arlo cameras, recordsVideoQuality, 1080p Full HD]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recordsVideoQuality Context triple: [Arlo cameras, recordsVideoQuality, 1080p Full HD]
-
A.
videoQuality
chosen
Indicates the level or standard of clarity, resolution, and overall visual fidelity associated with a given video.
-
B.
recordQuality
Indicates the assessed level or standard of quality associated with a particular record.
-
C.
videoStandard
Indicates the video format or broadcasting standard that applies to a given video or recording.
-
D.
videoEncoding
Indicates that one entity is used to encode, compress, or transform video data into a particular digital format or representation for another entity.
-
E.
imageQuality
Indicates the assessed level or degree of visual clarity, detail, and overall fidelity of an image.
- 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_69e75dbfff7081909b0aa779d48321d2 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f627aedf548190bc9f53c8a2d67b50 |
completed | May 2, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69f623a4e1048190bbb8dd1253fdcee9 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 21, 2026, 3:22 p.m.