Triple
T3345123
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwarzschild telescope |
E70354
|
entity |
| Predicate | mirrorShape |
P8982
|
FINISHED |
| Object | spherical mirrors |
—
|
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: spherical mirrors | Statement: [Schwarzschild telescope, mirrorShape, spherical mirrors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mirrorShape Context triple: [Schwarzschild telescope, mirrorShape, spherical mirrors]
-
A.
mirrorType
Indicates that one entity is a specific kind or category of mirror in relation to another entity.
-
B.
primaryMirrorShape
chosen
Indicates that one entity has a primary mirror whose geometric shape or curvature type is specified by the other entity.
-
C.
mirrorCount
Indicates the number of mirrors associated with or present in relation to a given entity or context.
-
D.
secondaryMirrorShape
Indicates that one entity specifies or defines the geometric shape of a secondary mirror associated with another entity.
-
E.
mirrorTechnology
Indicates a relationship where one technology closely reflects, imitates, or duplicates the functionality or design of another.
- 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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1f36c74819093ef2c74a46c2351 |
completed | March 8, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69ada42df1d48190874bb05f95deefde |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:12 p.m.