Triple
T15838824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolter type I grazing-incidence telescope |
E384049
|
entity |
| Predicate | mirrorConfiguration |
P14086
|
FINISHED |
| Object | coaxial 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: coaxial mirrors | Statement: [Wolter type I grazing-incidence telescope, mirrorConfiguration, coaxial mirrors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mirrorConfiguration Context triple: [Wolter type I grazing-incidence telescope, mirrorConfiguration, coaxial mirrors]
-
A.
mirrorType
Indicates that one entity is a specific kind or category of mirror in relation to another entity.
-
B.
mirrorTechnology
Indicates a relationship where one technology closely reflects, imitates, or duplicates the functionality or design of another.
-
C.
primaryMirrorConfiguration
chosen
Indicates the specific structural and optical setup used for a system’s primary mirror.
-
D.
mirrorCount
Indicates the number of mirrors associated with or present in relation to a given entity or context.
-
E.
mirrorCastBy
Indicates that one entity serves as a reflective counterpart or duplication of another, as if produced or defined by a mirror.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142e4fa24819086a1a226082ac2d3 |
completed | April 16, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69e005418f588190824d91ff7974dada |
completed | April 15, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:49 a.m.