Triple
T2831012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European Extremely Large Telescope |
E62234
|
entity |
| Predicate | primaryMirrorArea |
P42735
|
FINISHED |
| Object | approximately 978 square 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: approximately 978 square meters | Statement: [European Extremely Large Telescope, primaryMirrorArea, approximately 978 square meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryMirrorArea Context triple: [European Extremely Large Telescope, primaryMirrorArea, approximately 978 square meters]
-
A.
primaryMirrorShape
Indicates that one entity has a primary mirror whose geometric shape or curvature type is specified by the other entity.
-
B.
primaryMirrorConfiguration
Indicates the specific structural and optical setup used for a system’s primary mirror.
-
C.
secondaryMirrorShape
Indicates that one entity specifies or defines the geometric shape of a secondary mirror associated with another entity.
-
D.
primaryArea
Indicates that one entity is the main or most important area, domain, or field associated with another entity.
-
E.
primaryMirrorDiameter
Indicates the diameter of the primary mirror used in an optical system or instrument.
- 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_69ab4c3c39188190955b9c49d98463d8 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdebd5a2c81908f0e30a0ae0eb8df |
completed | March 7, 2026, 8:15 a.m. |
| PD | Predicate disambiguation | batch_69abdd0acab881909e8c25cbef83678c |
completed | March 7, 2026, 8:08 a.m. |
| PDg | Predicate description generation | batch_69abddccd50c8190a975942f46cfc01f |
completed | March 7, 2026, 8:11 a.m. |
Created at: March 6, 2026, 10:01 p.m.