Triple
T35227659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miami Medical |
E1017140
|
entity |
| Predicate | portraysDepartment |
P199472
|
FINISHED |
| Object | Level 1 trauma center |
—
|
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: Level 1 trauma center | Statement: [Miami Medical, portraysDepartment, Level 1 trauma center]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysDepartment Context triple: [Miami Medical, portraysDepartment, Level 1 trauma center]
-
A.
portraysOffice
Indicates that one entity depicts, represents, or shows an office (as a place, role, or position) associated with another entity.
-
B.
propDepartment
Indicates that one entity functions as a department or organizational subdivision associated with another entity.
-
C.
portraysGroup
Indicates that one entity depicts, represents, or visually illustrates a group of entities as its subject.
-
D.
coversDepartment
Indicates that one entity includes, encompasses, or has responsibility for a particular department within its scope.
-
E.
laterDepartment
Indicates that one department occurs or is considered after another in a defined ordering or sequence.
- 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_69f76de12e4c8190bc46b71a32858356 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff3b32ee148190a3ba3b7600943fef |
completed | May 9, 2026, 1:48 p.m. |
| PD | Predicate disambiguation | batch_69ff3a238af88190a2c2245e30299e48 |
completed | May 9, 2026, 1:44 p.m. |
| PDg | Predicate description generation | batch_69ff3b323a748190827165f1cb274141 |
completed | May 9, 2026, 1:48 p.m. |
Created at: May 3, 2026, 4:02 p.m.