Triple
T19118589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Corey |
E467975
|
entity |
| Predicate | taughtProfession |
P61611
|
FINISHED |
| Object | actors |
—
|
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: actors | Statement: [Jeff Corey, taughtProfession, actors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: taughtProfession Context triple: [Jeff Corey, taughtProfession, actors]
-
A.
taughtAs
Indicates that one entity served as a teacher or instructor for another entity in an educational or training context.
-
B.
taughtThat
chosen
Indicates that one entity provided instruction or education to another entity about a specific subject, skill, or concept.
-
C.
taughtThrough
Indicates that one entity provided instruction, education, or training to another entity by means of a specified method, medium, or intermediary.
-
D.
hasTeaching
Indicates that one entity provides instruction or educational guidance to another entity.
-
E.
leftProfession
Indicates that an entity has stopped or abandoned a particular profession or occupation they previously held.
- 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_69d8dd06a26481908039e2a1bae8c597 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e3c756a88190942930e6ae7242a7 |
completed | April 20, 2026, 8:28 a.m. |
| PD | Predicate disambiguation | batch_69e4b9b085288190b974d649e12e0844 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:05 p.m.