Triple
T2075027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zuckerberg San Francisco General Hospital and Trauma Center |
E44901
|
entity |
| Predicate | isSafetyNetHospital |
P34783
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Zuckerberg San Francisco General Hospital and Trauma Center, isSafetyNetHospital, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isSafetyNetHospital Context triple: [Zuckerberg San Francisco General Hospital and Trauma Center, isSafetyNetHospital, true]
-
A.
isSafetyNetProvider
Indicates that one entity serves as a safety net provider, offering essential or last-resort support or services to another entity or population.
-
B.
isPublicHospital
Indicates that a hospital is owned, funded, or operated by a government or public authority rather than by private entities.
-
C.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
-
D.
isTeachingHospitalFor
Indicates that one institution serves as a clinical training site or educational facility for another, typically a medical school or health education program.
-
E.
hasAffiliatedHospital
Indicates that one entity (typically a medical professional, clinic, or organization) is formally connected or associated with a particular hospital for professional or operational purposes.
- 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_69a88916c2b48190a5ca2e9b12cad3ed |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba116ea0819086d16c3913159e9e |
completed | March 7, 2026, 5:39 a.m. |
| PD | Predicate disambiguation | batch_69abb7b0edac8190a58eabee55f73deb |
completed | March 7, 2026, 5:29 a.m. |
| PDg | Predicate description generation | batch_69abb85fe7a08190b991b1f23bc34f93 |
completed | March 7, 2026, 5:32 a.m. |
Created at: March 4, 2026, 7:41 p.m.