Triple
T28710895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TIROS program |
E729828
|
entity |
| Predicate | usedImagingType |
P52562
|
FINISHED |
| Object | television cameras |
—
|
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: television cameras | Statement: [TIROS program, usedImagingType, television cameras]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedImagingType Context triple: [TIROS program, usedImagingType, television cameras]
-
A.
hasImagingType
chosen
Indicates the specific imaging modality or technique associated with or used in a given imaging procedure or result.
-
B.
usesCameraType
Indicates that one entity employs or operates a specific type or category of camera.
-
C.
usesImageModel
Indicates that one entity employs or relies on an image-based model (such as a computer vision or image generation model) in relation to another entity or task.
-
D.
imagingActivation
Indicates that an entity initiates or undergoes an activation process related to imaging or image-based functionality.
-
E.
usesImagery
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
- 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_69f043e7d5a4819094b18aca10b1e024 |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f7a225a77c81908f8953ccfeb14336 |
completed | May 3, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69f7a06d4f108190bae3ab9ae431d2c7 |
completed | May 3, 2026, 7:22 p.m. |
Created at: April 28, 2026, 5:48 a.m.