Triple
T20016727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | McMicken School of Design |
E494739
|
entity |
| Predicate | trained |
P3665
|
FINISHED |
| Object | Kenyon Cox |
—
|
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: Kenyon Cox | Statement: [McMicken School of Design, trained, Kenyon Cox]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenyon Cox Context triple: [McMicken School of Design, trained, Kenyon Cox]
-
A.
Kenyon Cox
chosen
Kenyon Cox was an American painter, muralist, and art critic of the late 19th and early 20th centuries, known for his classical style and influential public murals.
-
B.
Jim O'Heir
Jim O'Heir is an American actor best known for his comedic role as the bumbling yet lovable Jerry Gergich on the television series "Parks and Recreation."
-
C.
Douglas Fackler
Douglas Fackler is a bumbling, mild-mannered police cadet character from the "Police Academy" comedy film series.
-
D.
Michael Barnett
Michael Barnett is an international relations scholar known for his work on global governance, humanitarianism, and international organizations.
-
E.
Aaron Heilman
Aaron Heilman is a former American Major League Baseball pitcher best known for his years with the New York Mets in the 2000s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623cb8188190b95913ffed895930 |
completed | April 20, 2026, 5:28 p.m. |
Created at: April 11, 2026, 3:34 p.m.