Triple
T2602113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avera St. Luke’s Hospital |
E58367
|
entity |
| Predicate | emergencyDepartment |
P39556
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Avera St. Luke’s Hospital, emergencyDepartment, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: emergencyDepartment Context triple: [Avera St. Luke’s Hospital, emergencyDepartment, yes]
-
A.
emergencyOffice
Indicates that an office or location serves as an emergency contact point or coordination center for urgent or crisis situations.
-
B.
emergencyDefinitionSection
Indicates a section that defines or explains what constitutes an emergency within a given context or document.
-
C.
emergencyRole
Indicates that an entity holds a specific function, responsibility, or authority in emergency or crisis situations.
-
D.
emergencyResponse
Indicates a relationship where an entity takes immediate action to address or manage an urgent or critical situation.
-
E.
hasTraumaCenter
Indicates that an entity (such as a hospital or facility) includes or is equipped with a designated trauma center capable of providing specialized emergency care for severe injuries.
- 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_69ab4ac14040819098b13f4a27d5c8ff |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd459ca6c81908505be96d097b739 |
completed | March 7, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69abd0d4e8648190b612eb09aa085451 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1c7b6e48190be9a0c31069df797 |
completed | March 7, 2026, 7:20 a.m. |
Created at: March 6, 2026, 9:49 p.m.