Triple
T31456451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelley Kagan |
E802460
|
entity |
| Predicate | hasOnlineLectures |
P2505
|
FINISHED |
| Object | Open Yale Courses series on death |
—
|
NE NERFINISHED |
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: Open Yale Courses series on death | Statement: [Shelley Kagan, hasOnlineLectures, Open Yale Courses series on death]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnlineLectures Context triple: [Shelley Kagan, hasOnlineLectures, Open Yale Courses series on death]
-
A.
includesLecture
Indicates that one entity (such as a course, module, or event) contains or is composed of a specific lecture as part of its structure or content.
-
B.
hasOnlinePrograms
chosen
Indicates that an entity offers or provides programs, courses, or services that are available online.
-
C.
hasGivenLecturesOn
Indicates that one entity has delivered or presented lectures on a particular subject, topic, or field to an audience.
-
D.
numberOfLectures
Indicates the total count of lectures associated with a given entity or context.
-
E.
hasLecturer
Indicates that an educational course, class, or module is taught or overseen by a specific lecturer.
- 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_69f348c678ac81908a2e950867619061 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: April 30, 2026, 9:16 p.m.