Triple
T11176235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LSST Camera |
E264420
|
entity |
| Predicate | hasPixelCount |
P49584
|
FINISHED |
| Object | 3.2 gigapixels |
—
|
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: 3.2 gigapixels | Statement: [LSST Camera, hasPixelCount, 3.2 gigapixels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPixelCount Context triple: [LSST Camera, hasPixelCount, 3.2 gigapixels]
-
A.
hasPixelPitch
Indicates the distance between adjacent pixels in a display or image sensor, typically measured from center to center.
-
B.
pixelCount
chosen
Indicates the total number of individual pixels that make up a given image or visual element.
-
C.
hasNumberOfScreens
Indicates the quantity of screens associated with or contained in a given entity.
-
D.
hasColorModel
Indicates that an entity uses or is associated with a particular color representation model (such as RGB, CMYK, or HSV) for defining its colors.
-
E.
hasNumberOfPoints
Indicates that an entity is associated with a specific count of points it possesses or comprises.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8987e1081909b28a0bdb866beae |
completed | April 9, 2026, 5:57 p.m. |
| PD | Predicate disambiguation | batch_69d75cf0e6e88190973694abe2990973 |
completed | April 9, 2026, 8:01 a.m. |
Created at: April 8, 2026, 9:29 p.m.