Triple
T32196197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LUCERNE |
E822410
|
entity |
| Predicate | hasConditionCategory |
—
|
GENERATED |
| Object | retinal disease |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConditionCategory Context triple: [LUCERNE, hasConditionCategory, retinal disease]
-
A.
hasCondition
chosen
Indicates that an entity possesses, experiences, or is affected by a particular condition or state.
-
B.
haveCategoryCode
Indicates that an entity is associated with a specific classification or category identified by a code.
-
C.
containsCondition
Indicates that one entity includes, embodies, or is associated with a particular condition.
-
D.
hasCategoryOn
Indicates that something is assigned to or associated with a specific category within a given context or scope.
-
E.
canCondition
Indicates that one entity has the capability or potential to impose, apply, or bring about a particular condition on another entity or situation.
- F. None of above.
Provenance (1 batch)
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_69f349093174819086e633c190a51aa8 |
completed | April 30, 2026, 12:20 p.m. |
Created at: May 1, 2026, 12:36 a.m.