Triple
T20784103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corona Graeca |
E511582
|
entity |
| Predicate | hasNumberOfEnamelPanels |
P141528
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Corona Graeca, hasNumberOfEnamelPanels, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfEnamelPanels Context triple: [Corona Graeca, hasNumberOfEnamelPanels, 8]
-
A.
hasNumberOfInscribedPanels
Indicates the relationship that specifies how many inscribed panels are associated with a given entity.
-
B.
numberOfPanels
Indicates the total count of distinct panels associated with or contained within a given entity.
-
C.
numberOfConcretePanels
Indicates the total count of concrete panels associated with or used in relation to a given entity.
-
D.
hasNumberOfVocationalPanels
Indicates the relationship specifying how many vocational panels are associated with a given entity.
-
E.
hasSculptedPanels
Indicates that an entity features or includes panels that have been sculpted as part of its design or structure.
- 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_69e0b4cac7a48190a715cb3d545df2b4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c28a4584819084d2d02febe47001 |
completed | April 21, 2026, 12:19 a.m. |
| PD | Predicate disambiguation | batch_69e5c0550ec481908a0877fb2409d983 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3cbe5788190b7ace43bfdac2ef6 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:38 p.m.