Triple
T3631137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kepler space telescope |
E76957
|
entity |
| Predicate | aperture |
P12152
|
FINISHED |
| Object | 0.95 metres |
—
|
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: 0.95 metres | Statement: [Kepler space telescope, aperture, 0.95 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: aperture Context triple: [Kepler space telescope, aperture, 0.95 metres]
-
A.
hasAperture
chosen
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
B.
rearCameraAperture
Indicates the size or f-stop value of the aperture used by a device’s rear-facing camera when capturing images or video.
-
C.
hasApertureClass
Indicates that one entity is classified according to a specific aperture category or class of another entity.
-
D.
focalLength
Indicates the distance between a lens or mirror and its focal point, determining how strongly it converges or diverges light.
-
E.
exposureTime
Indicates the duration for which a subject or object is exposed to a particular condition, influence, or medium.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc30136e88190922bb542971b5239 |
completed | March 8, 2026, 6:42 p.m. |
| PD | Predicate disambiguation | batch_69adb8410a5881909c94818d7060b2b0 |
completed | March 8, 2026, 5:56 p.m. |
Created at: March 8, 2026, 3:23 p.m.