Triple
T5892452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Providence Health & Services |
E131021
|
entity |
| Predicate | hasNumberOfClinics |
P67173
|
FINISHED |
| Object | hundreds of clinics |
—
|
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: hundreds of clinics | Statement: [Providence Health & Services, hasNumberOfClinics, hundreds of clinics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfClinics Context triple: [Providence Health & Services, hasNumberOfClinics, hundreds of clinics]
-
A.
hasNumberOfCentres
Indicates the relationship specifying how many centers (or central units/locations) are associated with a given entity.
-
B.
numberOfHospitals
Indicates the total count of hospitals associated with a given entity or within a specified context.
-
C.
numberOfStores
Indicates the total count of stores associated with a given entity or context.
-
D.
hasNumberOfCinemas
Indicates the quantity of cinemas associated with a given entity.
-
E.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
- 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_69c00857439c819095950754176aa58a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0400f1af881908d376ea4793f6dea |
completed | March 22, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69c0334dc8248190b7394dcece362d52 |
completed | March 22, 2026, 6:22 p.m. |
| PDg | Predicate description generation | batch_69c0400dbec08190b2ef73689b2c0c31 |
completed | March 22, 2026, 7:16 p.m. |
Created at: March 22, 2026, 3:58 p.m.