Triple
T15838851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolter type I grazing-incidence telescope |
E384049
|
entity |
| Predicate | typicalMirrorCoating |
P81140
|
FINISHED |
| Object | gold |
—
|
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: gold | Statement: [Wolter type I grazing-incidence telescope, typicalMirrorCoating, gold]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalMirrorCoating Context triple: [Wolter type I grazing-incidence telescope, typicalMirrorCoating, gold]
-
A.
mirrorType
Indicates that one entity is a specific kind or category of mirror in relation to another entity.
-
B.
coatingType
chosen
Indicates the type or kind of coating applied to or associated with an entity.
-
C.
secondaryMirrorDesignation
Indicates the specific label or identifier assigned to a secondary mirror in an optical or telescope system.
-
D.
secondaryMirrorShape
Indicates that one entity specifies or defines the geometric shape of a secondary mirror associated with another entity.
-
E.
secondaryMirrorDiameter
Indicates the diameter measurement of a system’s secondary mirror in a multi-mirror optical setup.
- 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.