Triple
T12114987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint Joseph Berea Hospital |
E288533
|
entity |
| Predicate | hasCareFocus |
P60450
|
FINISHED |
| Object | community healthcare |
—
|
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: community healthcare | Statement: [Saint Joseph Berea Hospital, hasCareFocus, community healthcare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCareFocus Context triple: [Saint Joseph Berea Hospital, hasCareFocus, community healthcare]
-
A.
hasCareModel
Indicates that one entity uses, follows, or is governed by a particular model or approach to providing care.
-
B.
hasCoverageFocus
chosen
Indicates that one entity’s coverage, attention, or analysis is specifically focused on or directed toward another entity.
-
C.
hasAgeFocus
Indicates a relationship where something is characterized or distinguished by a particular age group or age-related emphasis.
-
D.
focusesOnMedicalCare
Indicates that one entity directs attention, resources, or activity specifically toward providing or improving medical care for another entity.
-
E.
requiresCare
Indicates that one entity depends on another to provide care, attention, or maintenance for its proper functioning or well-being.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.