Triple
T4107696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ciechocinek |
E88493
|
entity |
| Predicate | hasMedicalSpecialization |
P466
|
FINISHED |
| Object | respiratory diseases treatment |
—
|
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: respiratory diseases treatment | Statement: [Ciechocinek, hasMedicalSpecialization, respiratory diseases treatment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalSpecialization Context triple: [Ciechocinek, hasMedicalSpecialization, respiratory diseases treatment]
-
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.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
D.
medicalAchievement
Indicates that an entity has accomplished a notable success, breakthrough, or recognized contribution in the field of medicine or healthcare.
-
E.
professionServed
Indicates that an entity has performed work or provided services in a particular profession or occupational role.
- 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_69aed9484fb881909146f4c772ad277c |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03d7240c8190a64dcbc669772808 |
completed | March 9, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69af0183eb84819087d7184de28f5514 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:40 p.m.