Triple
T7817478
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MMT Observatory |
E181046
|
entity |
| Predicate | originalIndividualMirrorDiameter |
P2517
|
FINISHED |
| Object | 1.8 meters |
—
|
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: 1.8 meters | Statement: [MMT Observatory, originalIndividualMirrorDiameter, 1.8 meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalIndividualMirrorDiameter Context triple: [MMT Observatory, originalIndividualMirrorDiameter, 1.8 meters]
-
A.
primaryMirrorDiameter
chosen
Indicates the diameter of the primary mirror used in an optical system or instrument.
-
B.
primaryMirrorShape
Indicates that one entity has a primary mirror whose geometric shape or curvature type is specified by the other entity.
-
C.
objectiveLensDiameter
Indicates the diameter of the objective lens used in an optical device, such as a camera or telescope.
-
D.
primaryMirrorCentralHole
Indicates that the primary mirror has a central hole, typically for allowing light or instrumentation to pass through.
-
E.
mirrorType
Indicates that one entity is a specific kind or category of mirror in relation to another entity.
- 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_69ca828153f48190bdb27ac46f8e0745 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf9708bdc8190a5154efe0f96f458 |
completed | March 30, 2026, 10:30 p.m. |
| PD | Predicate disambiguation | batch_69cae91687788190af9cb7aaa996d291 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 4:40 p.m.