Triple
T2942076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dečani Monastery |
E79408
|
entity |
| Predicate | numberOfFrescoScenesApprox |
P5764
|
FINISHED |
| Object | 1000 |
—
|
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: 1000 | Statement: [Dečani Monastery, numberOfFrescoScenesApprox, 1000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFrescoScenesApprox Context triple: [Dečani Monastery, numberOfFrescoScenesApprox, 1000]
-
A.
hasFrescoes
Indicates that something contains or is adorned with fresco paintings as part of its structure or decoration.
-
B.
estimatedNumberOfPaintings
chosen
Indicates the approximate count of paintings associated with an entity, rather than an exact, verified number.
-
C.
numberOfStainedGlassPanels
Indicates the count of stained glass panels associated with a given entity or object.
-
D.
numberOfFiguresDepicted
Indicates the total count of distinct figures shown within a given depiction or representation.
-
E.
numberOfSculptures
Indicates the quantity of sculptures associated with a given 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_69ad8b1089588190b74d9e2505e45762 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9870e5d08190b3b277ba823fe6a1 |
completed | March 8, 2026, 3:40 p.m. |
| PD | Predicate disambiguation | batch_69ad96088fb481909976b436c2b729d9 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:56 p.m.