Triple
T257067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | du Pont Telescope |
E5457
|
entity |
| Predicate | mirrorCount |
P8981
|
FINISHED |
| Object | 1 primary mirror |
—
|
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 primary mirror | Statement: [du Pont Telescope, mirrorCount, 1 primary mirror]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mirrorCount Context triple: [du Pont Telescope, mirrorCount, 1 primary mirror]
-
A.
arrowCount
Indicates the number of arrows associated with or involved in a given entity or interaction.
-
B.
hasSecondaryMirrorPosition
Indicates the spatial placement or configuration of a secondary mirror relative to the primary optical system.
-
C.
shaftCount
Indicates the number of shafts associated with or contained in an object or system.
-
D.
collectionSize
Indicates the total number of items contained within a specified collection.
-
E.
telescopeArrayMemberCount
Indicates the number of individual telescopes that are part of a given telescope array.
- 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_69a2580a64ac8190ad76e34bb0715b5e |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25d5884c88190a349d7593b688921 |
completed | Feb. 28, 2026, 3:13 a.m. |
| PD | Predicate disambiguation | batch_69a25b694c08819085bb4b256fa7736f |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25c4b773c81908f1017f40b0bfd07 |
completed | Feb. 28, 2026, 3:08 a.m. |
Created at: Feb. 28, 2026, 2:55 a.m.