Triple
T24844548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dominican Sisters of Hawthorne |
E621709
|
entity |
| Predicate | patientsServed |
P68932
|
FINISHED |
| Object | terminally ill |
—
|
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: terminally ill | Statement: [Dominican Sisters of Hawthorne, patientsServed, terminally ill]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: patientsServed Context triple: [Dominican Sisters of Hawthorne, patientsServed, terminally ill]
-
A.
numberOfAnnualPatientVisits
Indicates the total count of patient visits that occur over the course of one year.
-
B.
servedPerson
chosen
Indicates that one entity has provided service, assistance, or attention to another entity.
-
C.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
-
D.
treatsPatientsFrom
Indicates that a healthcare provider gives medical treatment or services to patients originating from a particular location or group.
-
E.
numberOfHospitalized
Indicates the count of individuals who have been admitted to a hospital for medical care.
- 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_69e2fac185d48190a0a6073ad1f6b792 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f453035f508190be83a3d521723acf |
completed | May 1, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f44d6ef33081908f5d36ba1ae5f473 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 5:19 a.m.