Triple
T28289451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gran Telescopio Canarias |
E713381
|
entity |
| Predicate | numberOfMirrorSegments |
P14317
|
FINISHED |
| Object | 36 |
—
|
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: 36 | Statement: [Gran Telescopio Canarias, numberOfMirrorSegments, 36]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMirrorSegments Context triple: [Gran Telescopio Canarias, numberOfMirrorSegments, 36]
-
A.
numberOfPrimaryMirrorSegments
chosen
Indicates the total count of individual segments that make up a system’s primary mirror.
-
B.
mirrorCount
Indicates the number of mirrors associated with or present in relation to a given entity or context.
-
C.
hasNumberOfPrimaryMirrors
Indicates the relationship specifying how many primary mirrors are associated with a given entity.
-
D.
mirrorsStructureOf
Indicates that one entity’s overall organization, pattern, or arrangement closely corresponds to and reflects the structure of another entity.
-
E.
hasMirrorMotif
Indicates that one entity features a mirror-related motif or pattern in relation to another entity or context.
- 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_69efb52371d88190a1381c4e58a3b731 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69ff5285ed74819097e6e2a9084a079a |
completed | May 9, 2026, 3:28 p.m. |
| PD | Predicate disambiguation | batch_69ff51fbe28881908ac8417dff9db81a |
completed | May 9, 2026, 3:25 p.m. |
Created at: April 27, 2026, 11:28 p.m.