Triple
T18276851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Coburn |
E437758
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Coburn |
—
|
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: Coburn | Statement: [Tom Coburn, familyName, Coburn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coburn Context triple: [Tom Coburn, familyName, Coburn]
-
A.
Coburn
chosen
Coburn is a surname and given name that serves as an alternative spelling of Cockburn, borne by various notable individuals in politics, entertainment, and other fields.
-
B.
Crapo
Crapo is the middle name of William C. Durant, the American industrialist who co-founded General Motors and Chevrolet.
-
C.
Bunning
Bunning is a surname most notably associated with Jim Bunning, a Hall of Fame Major League Baseball pitcher and former U.S. Senator from Kentucky.
-
D.
Arlen
Arlen is a given name most notably borne by Arlen Specter, a long-serving United States Senator from Pennsylvania.
-
E.
Kassebaum
Kassebaum is the surname most prominently associated with Nancy Kassebaum, a former United States senator from Kansas known for her moderate Republican views and bipartisan work.
- 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_69d8b914530c8190b4474d862a2b2a1b |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e500528bb88190a9f9ba6428cc2076 |
completed | April 19, 2026, 4:18 p.m. |
Created at: April 10, 2026, 10:34 a.m.