Triple
T7555763
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Army Nurse Corps |
E178661
|
entity |
| Predicate | typicalSpecialties |
P466
|
FINISHED |
| Object | medical-surgical nursing |
—
|
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: medical-surgical nursing | Statement: [Army Nurse Corps, typicalSpecialties, medical-surgical nursing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSpecialties Context triple: [Army Nurse Corps, typicalSpecialties, medical-surgical nursing]
-
A.
hasSpecialty
chosen
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
C.
professionServed
Indicates that an entity has performed work or provided services in a particular profession or occupational role.
-
D.
consultsOn
Indicates that one entity provides expert advice, guidance, or professional input to another entity regarding a specific subject, project, or decision.
-
E.
practicedMedicineIn
Indicates that a person engaged in the professional practice of medicine within a specified location or jurisdiction.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8d82dd481908351876edc70c4ec |
completed | March 27, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69c6f4dc485c819080da13e3b7f4f08f |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:49 p.m.