Triple
T9116259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Loma Linda |
E218724
|
entity |
| Predicate | hasHealthcareSector |
P53279
|
FINISHED |
| Object | major regional medical hub |
—
|
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: major regional medical hub | Statement: [City of Loma Linda, hasHealthcareSector, major regional medical hub]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHealthcareSector Context triple: [City of Loma Linda, hasHealthcareSector, major regional medical hub]
-
A.
hasHealthcareProvider
Indicates that one entity receives healthcare services or medical oversight from another entity acting as its healthcare provider.
-
B.
hasHealthcareInfrastructure
Indicates that an entity possesses facilities, systems, and resources necessary to deliver healthcare services.
-
C.
healthcareType
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
-
D.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
E.
containsIndustry
chosen
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
- 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_69ca83dc94ac8190b9ef42684d36ff39 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8a4c9e08190ba3603a5d00afb20 |
completed | April 1, 2026, 5:09 a.m. |
| PD | Predicate disambiguation | batch_69cc66003e3c819091e1e42c9cf7c781 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:17 p.m.