Triple
T5317987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mountains (Planet Earth II) |
E121597
|
entity |
| Predicate | featuresCinematographyTechnique |
P2760
|
FINISHED |
| Object | aerial photography |
—
|
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: aerial photography | Statement: [Mountains (Planet Earth II), featuresCinematographyTechnique, aerial photography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCinematographyTechnique Context triple: [Mountains (Planet Earth II), featuresCinematographyTechnique, aerial photography]
-
A.
filmingTechnique
chosen
Indicates the specific method or style used to capture visual content during the filming process.
-
B.
cinematographyBy
Indicates that the cinematographic work (such as the camera work or visual style of a film or video) is created or supervised by a specified person or entity.
-
C.
cinematographyAwardedTo
Indicates that a cinematography-related award has been given to a particular recipient (such as a person or team) for their work.
-
D.
featuresTechnique
Indicates that something incorporates or makes use of a particular technique as part of its content or execution.
-
E.
filmSetting
Indicates the place, time, or environment in which the events of a film are set or take place.
- 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_69bd463d956c819088105c3db802c017 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd86f20f008190be7b5848af05f2b8 |
completed | March 20, 2026, 5:42 p.m. |
| PD | Predicate disambiguation | batch_69bd84561c7081909e5937c7816e492c |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 1:59 p.m.