Triple
T11176233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LSST Camera |
E264420
|
entity |
| Predicate | hasEffectiveAperture |
P12152
|
FINISHED |
| Object | 6.5 meters |
—
|
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: 6.5 meters | Statement: [LSST Camera, hasEffectiveAperture, 6.5 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEffectiveAperture Context triple: [LSST Camera, hasEffectiveAperture, 6.5 meters]
-
A.
hasAperture
chosen
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
B.
hasApertureClass
Indicates that one entity is classified according to a specific aperture category or class of another entity.
-
C.
hasApertureShape
Indicates that an entity’s aperture (opening) has a specific geometric or descriptive shape.
-
D.
hasFocalRatio
Indicates a relationship where an optical system is associated with a specific focal ratio (f-number) that characterizes its light-gathering speed and image brightness.
-
E.
hasFocalRatioRange
Indicates that an entity is associated with a range of possible focal ratios, specifying the minimum and maximum f-number values it can have.
- 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.