Triple
T19022319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FV104 Samaritan |
E465515
|
entity |
| Predicate | medicalEquipment |
P101141
|
FINISHED |
| Object | stretcher racks |
—
|
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: stretcher racks | Statement: [FV104 Samaritan, medicalEquipment, stretcher racks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalEquipment Context triple: [FV104 Samaritan, medicalEquipment, stretcher racks]
-
A.
medicalBackground
Indicates that an entity has a history of prior medical conditions, treatments, or health-related experiences relevant to its current state or context.
-
B.
medicalCar
Indicates that an entity is a vehicle used for providing medical transport or emergency medical services to another entity.
-
C.
typeOfEquipmentUsed
chosen
Indicates that a particular piece or category of equipment is utilized in performing a specific action, process, or activity.
-
D.
medicalEvent
Indicates that a specific health-related occurrence or clinical incident has taken place involving one or more entities.
-
E.
medicalPractice
Indicates a relationship where an entity engages in or carries out the professional provision of medical care or services.
- 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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6dfa3c88190a057c3385d680cf8 |
completed | April 20, 2026, 7:33 a.m. |
| PD | Predicate disambiguation | batch_69e4a2fd80c081908237317a3a883e1c |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 12:02 p.m.