Triple
T17723817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leslie Hamilton Gearren |
E442406
|
entity |
| Predicate | primaryCareer |
P24248
|
FINISHED |
| Object | healthcare |
—
|
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: healthcare | Statement: [Leslie Hamilton Gearren, primaryCareer, healthcare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryCareer Context triple: [Leslie Hamilton Gearren, primaryCareer, healthcare]
-
A.
careerType
Indicates the kind or category of professional occupation or career path associated with an entity.
-
B.
careerField
chosen
Indicates the professional domain or occupational area in which an entity works or specializes.
-
C.
primaryWork
Indicates that one work is the main or most significant work associated with a given entity, as opposed to other secondary or related works.
-
D.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
-
E.
managedCareerOf
Indicates that one entity was responsible for overseeing, directing, or handling the professional career of another entity.
- 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_69d8b9ec79688190b86bdcef85a7b3aa |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4748900608190bf5ba04415edaffc |
completed | April 19, 2026, 6:22 a.m. |
| PD | Predicate disambiguation | batch_69e3cde815e08190881972e2d80d151e |
completed | April 18, 2026, 6:31 p.m. |
Created at: April 10, 2026, 10:07 a.m.