Triple
T9221015
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Egyptian Ambulance Organization |
E221358
|
entity |
| Predicate | staffType |
P21064
|
FINISHED |
| Object | emergency medical technicians |
—
|
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: emergency medical technicians | Statement: [Egyptian Ambulance Organization, staffType, emergency medical technicians]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: staffType Context triple: [Egyptian Ambulance Organization, staffType, emergency medical technicians]
-
A.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
B.
staffIncluded
Indicates that staff members are included or provided as part of the associated entity, service, or arrangement.
-
C.
crewType
chosen
Indicates the specific role or category of crew associated with an entity, such as the type of personnel assigned to operate or support it.
-
D.
peakPersonnel
Indicates the maximum number of personnel involved or present at any point during a specified period or activity.
-
E.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
- 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_69ca83eae42c8190a0ea9e040710a277 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda75b6888190814cfb890e763b8f |
completed | April 1, 2026, 8:42 a.m. |
| PD | Predicate disambiguation | batch_69cc7a3daeb481908b0abde3fbc1f1f0 |
completed | April 1, 2026, 1:51 a.m. |
Created at: March 30, 2026, 7:28 p.m.