Triple
T8891470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buford "Mad Dog" Tannen |
E211686
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Buford |
E744780
|
NE 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: Buford | Statement: [Buford "Mad Dog" Tannen, givenName, Buford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buford Context triple: [Buford "Mad Dog" Tannen, givenName, Buford]
-
A.
Buford
chosen
Buford is a surname most notably associated with Union cavalry officer John Buford, a key figure in the American Civil War.
-
B.
Buford, Georgia
Buford, Georgia is a small city in the Atlanta metropolitan area known for the Mall of Georgia and its historic downtown district.
-
C.
Marietta
Marietta is a feminine given name, often considered a diminutive or variant of names like Maria or Marita, used in various European and English-speaking cultures.
-
D.
Macon
Macon is a surname of English and French origin borne by various notable individuals, including American statesman Nathaniel Macon.
-
E.
Macon
Macon is a small town located in Warren County, North Carolina, known for its rural character and proximity to Lake Gaston.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca83907954819096d52a245b635841 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc61ba33c48190a657fc4147a326c0 |
completed | April 1, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfabf01d048190bed52b5b001d7ffa |
completed | April 3, 2026, noon |
Created at: March 30, 2026, 6:54 p.m.