Triple
T18822402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macon Dead II |
E460293
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Macon |
—
|
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: Macon | Statement: [Macon Dead II, givenName, Macon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macon Context triple: [Macon Dead II, givenName, Macon]
-
A.
Macon
chosen
Macon is a surname of English and French origin borne by various notable individuals, including American statesman Nathaniel Macon.
-
B.
Macon
Macon is a small town located in Warren County, North Carolina, known for its rural character and proximity to Lake Gaston.
-
C.
Macon metropolitan area
The Macon metropolitan area is a regional urban and economic hub in central Georgia centered on the city of Macon and its surrounding communities.
-
D.
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.
-
E.
Dawsonville
Dawsonville is a small city in north Georgia known for its gold rush history and strong ties to stock car racing and NASCAR culture.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6bbc7148190819252071a765975 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:55 a.m.