Triple
T28194676
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bachelor of Dental Surgery |
E716410
|
entity |
| Predicate | includesInternship |
P155286
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Bachelor of Dental Surgery, includesInternship, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesInternship Context triple: [Bachelor of Dental Surgery, includesInternship, yes]
-
A.
internship
Indicates that one entity is engaged in a temporary, often educational work placement or training position with another entity, typically to gain practical experience.
-
B.
hasPracticum
chosen
Indicates that an entity includes, requires, or is associated with a practical training or hands-on learning component.
-
C.
isInterviewBased
Indicates that something (such as a study, article, or decision) is based primarily on information gathered through interviews.
-
D.
offersApprenticeshipTraining
Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
-
E.
isFellowInternOf
Indicates that two individuals share the same internship position or program during an overlapping time period.
- 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_69efd6b612f48190a72012b520afbd10 |
completed | April 27, 2026, 9:35 p.m. |
| NER | Named-entity recognition | batch_69fecb4d02f881909a9ee97ce98000d5 |
completed | May 9, 2026, 5:51 a.m. |
| PD | Predicate disambiguation | batch_69fec9846c1c8190b317f0711f0755db |
completed | May 9, 2026, 5:43 a.m. |
Created at: April 27, 2026, 10:27 p.m.