Triple
T22719199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Large Binocular Telescope |
E561813
|
entity |
| Predicate | hasMirrorTechnology |
P149449
|
FINISHED |
| Object | lightweight honeycomb primary 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: lightweight honeycomb primary mirrors | Statement: [Large Binocular Telescope, hasMirrorTechnology, lightweight honeycomb primary mirrors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMirrorTechnology Context triple: [Large Binocular Telescope, hasMirrorTechnology, lightweight honeycomb primary mirrors]
-
A.
hasMirrorSupportSystem
Indicates that an entity is equipped with or connected to a system that supports or stabilizes a mirror.
-
B.
supportsMirrorMode
Indicates that one entity provides the capability for another entity or process to operate in a mirror or mirrored-display mode.
-
C.
mirrorTechnology
Indicates a relationship where one technology closely reflects, imitates, or duplicates the functionality or design of another.
-
D.
hasSecondaryMirrorType
Indicates that an entity’s secondary mirror is of a specified type or design.
-
E.
hasMirrorOrLensMaterial
Indicates that an object’s mirror or lens component is made from a specified material.
- 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_69e2454fc984819088213b58ee87a002 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1790fbf9c819082ba7b48801a7b39 |
completed | April 29, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69eed2a971c0819088af574e40c9343f |
completed | April 27, 2026, 3:06 a.m. |
| PDg | Predicate description generation | batch_69eeeb5681f88190821129ced752f190 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:19 p.m.