Triple
T15982702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medical Academy of Łódź |
E387613
|
entity |
| Predicate | employedProfession |
P2374
|
FINISHED |
| Object | physician |
—
|
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: physician | Statement: [Medical Academy of Łódź, employedProfession, physician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employedProfession Context triple: [Medical Academy of Łódź, employedProfession, physician]
-
A.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
B.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
C.
employedRole
Indicates that an entity holds or performs a specific role or position within an employment or work context.
-
D.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
E.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e17d4d08f481909f38b75e3f42d9ab |
completed | April 17, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69e142d9d8e881909b559a3e3ca21d24 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.