Triple
T26551514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ceylon Volunteer Medical Corps |
E671688
|
entity |
| Predicate | usedMedicalPersonnel |
P162807
|
FINISHED |
| Object | doctors |
—
|
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: doctors | Statement: [Ceylon Volunteer Medical Corps, usedMedicalPersonnel, doctors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedMedicalPersonnel Context triple: [Ceylon Volunteer Medical Corps, usedMedicalPersonnel, doctors]
-
A.
administeredInPracticeBy
Indicates that a medical treatment, procedure, or intervention is carried out or delivered by a specific healthcare practice or provider entity.
-
B.
hasMedicalStaffApprox
Indicates that an entity is associated with an approximate or estimated number of medical staff.
-
C.
usesMedicalKnowledge
Indicates that an entity applies or relies on medical knowledge in performing an action or making a decision.
-
D.
hasMedicalAttendant
Indicates that one entity serves as a medical attendant (e.g., providing medical care or supervision) for another entity.
-
E.
clinicalRole
Indicates the specific function, responsibility, or position an entity holds within a clinical or healthcare 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_69eeb32163f08190af5f81282738e27a |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f62e4168a48190b45268f922780da6 |
completed | May 2, 2026, 5:02 p.m. |
| PD | Predicate disambiguation | batch_69f62c15952881908a5ea0c25904afec |
completed | May 2, 2026, 4:53 p.m. |
| PDg | Predicate description generation | batch_69f62d5268ac8190835dc7119353b840 |
completed | May 2, 2026, 4:58 p.m. |
Created at: April 27, 2026, 1:47 a.m.