Triple
T11176249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LSST Camera |
E264420
|
entity |
| Predicate | hasExposureTime |
P38789
|
FINISHED |
| Object | 15 seconds |
—
|
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: 15 seconds | Statement: [LSST Camera, hasExposureTime, 15 seconds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasExposureTime Context triple: [LSST Camera, hasExposureTime, 15 seconds]
-
A.
exposureTime
chosen
Indicates the duration for which a subject or object is exposed to a particular condition, influence, or medium.
-
B.
hasNumberOfStills
Indicates that an entity is associated with a specific count of still images or frames.
-
C.
hasExposuresIn
Indicates that an entity is subject to or involved in certain risks, conditions, or influencing factors within a specified context, environment, or domain.
-
D.
hasAperture
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
E.
exposureModes
Indicates the different ways or conditions under which an entity can be exposed to another entity, factor, or influence.
- 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.