Triple
T20310288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosies |
E510218
|
entity |
| Predicate | hasMedicalUnit |
P139615
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Rosies, hasMedicalUnit, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalUnit Context triple: [Rosies, hasMedicalUnit, yes]
-
A.
hasClinicalUnit
Indicates that an entity is associated with or belongs to a specific clinical unit or department within a healthcare setting.
-
B.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
C.
hasAffiliatedHospital
Indicates that one entity (typically a medical professional, clinic, or organization) is formally connected or associated with a particular hospital for professional or operational purposes.
-
D.
hasMaternityUnit
Indicates that a facility or organization includes or operates a maternity unit where childbirth and related maternal care services are provided.
-
E.
hasHospitalType
Indicates that a hospital is classified as belonging to a specific type or category (e.g., general, specialized, teaching).
- 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_69e0b4c7491c8190961113c4283b10b0 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e677436af88190a046c44fa45b68b4 |
completed | April 20, 2026, 6:58 p.m. |
| PD | Predicate disambiguation | batch_69e55b21b09081909e46691b6f45a07f |
completed | April 19, 2026, 10:45 p.m. |
| PDg | Predicate description generation | batch_69e56702ad04819099c1c08f28d16809 |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 16, 2026, 11:19 a.m.