Triple
T17693175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karl Kennedy |
E441085
|
entity |
| Predicate | hasProfessionInSeries |
P80683
|
FINISHED |
| Object | medical practitioner |
—
|
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: medical practitioner | Statement: [Karl Kennedy, hasProfessionInSeries, medical practitioner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionInSeries Context triple: [Karl Kennedy, hasProfessionInSeries, medical practitioner]
-
A.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
B.
portrayedByProfession
Indicates that an entity is depicted or represented by someone acting in a specified professional capacity.
-
C.
starOccupationInSeries
chosen
Indicates that an individual has a specific occupation or role as a starring character within a particular series.
-
D.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
E.
hasFictionalProfessionLevel
Indicates that an entity holds a fictional or imagined profession at a specified level, rank, or degree of expertise.
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47153a5c8819095c36fd414167fb1 |
completed | April 19, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
Created at: April 10, 2026, 10:03 a.m.