Triple
T7272725
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 140 Foot Telescope |
E161143
|
entity |
| Predicate | hasApertureShape |
P76138
|
FINISHED |
| Object | circular |
—
|
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: circular | Statement: [140 Foot Telescope, hasApertureShape, circular]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApertureShape Context triple: [140 Foot Telescope, hasApertureShape, circular]
-
A.
hasAperture
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.
rearCameraAperture
Indicates the size or f-stop value of the aperture used by a device’s rear-facing camera when capturing images or video.
-
D.
hasFocalPlane
Indicates that an optical system or imaging device possesses a specific focal plane where light is brought into focus.
-
E.
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.
- F. None of above. chosen
Provenance (4 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_69c6885181008190b419040e22939c7c |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6eb8a0b4881908ff27c5a75bd4a95 |
completed | March 27, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69c6e76a84a081908d4184c55b728e48 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6eb88a2648190acc79eeee8733705 |
completed | March 27, 2026, 8:41 p.m. |
Created at: March 27, 2026, 2:58 p.m.