Triple
T14981634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matt Kaufmann |
E373588
|
entity |
| Predicate | hasGivenTutorialOn |
P61611
|
FINISHED |
| Object | ACL2 theorem proving |
—
|
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: ACL2 theorem proving | Statement: [Matt Kaufmann, hasGivenTutorialOn, ACL2 theorem proving]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGivenTutorialOn Context triple: [Matt Kaufmann, hasGivenTutorialOn, ACL2 theorem proving]
-
A.
hasTutorialIn
Indicates that one entity provides or includes a tutorial within the context or medium of another entity.
-
B.
taughtThrough
Indicates that one entity provided instruction, education, or training to another entity by means of a specified method, medium, or intermediary.
-
C.
taughtThat
chosen
Indicates that one entity provided instruction or education to another entity about a specific subject, skill, or concept.
-
D.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
-
E.
demonstratedOn
Indicates that an action, behavior, or property is shown, illustrated, or proven using a particular entity as the example or subject.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6fe42a081909308f788fdf024d5 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:52 a.m.