Triple
T25511005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gabrielle Van der Mal |
E639377
|
entity |
| Predicate | professionInCongo |
P2374
|
FINISHED |
| Object | surgical nurse |
—
|
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: surgical nurse | Statement: [Gabrielle Van der Mal, professionInCongo, surgical nurse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionInCongo Context triple: [Gabrielle Van der Mal, professionInCongo, surgical nurse]
-
A.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
B.
commonProfessionAmongBearers
Indicates that multiple entities sharing a given attribute (such as a name or title) are frequently associated with the same profession.
-
C.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
D.
roleInNigeria
Indicates that an entity holds or has held a specific role, position, or function within the context of Nigeria.
-
E.
natureOfOccupation
Indicates the type or character of a person's occupation, describing what kind of work or role it is rather than who performs it.
- 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_69e75dbd09308190b6b5f0afdc12ec6d |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f80b05ac8190a4a0cd75e8717917 |
completed | May 2, 2026, 1:11 p.m. |
| PD | Predicate disambiguation | batch_69f468421ba08190880eac99135e5970 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 2:49 p.m.