Triple
T6049022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Cantey Sumter |
E134740
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Sumter
Sumter is a surname of English origin most notably associated with several prominent American historical figures and families.
|
E564545
|
NE FINISHED |
How this triple was built (4 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: Sumter | Statement: [Mary Cantey Sumter, familyName, Sumter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sumter Context triple: [Mary Cantey Sumter, familyName, Sumter]
-
A.
Sumter, South Carolina
Sumter, South Carolina is a small city in central South Carolina known for its military presence, including hosting the headquarters of United States Army Central at nearby Shaw Air Force Base.
-
B.
Palmetto
Palmetto is a long-distance Amtrak passenger train service operating along the U.S. East Coast between New York City and Savannah, Georgia.
-
C.
Sumter, Georgia
Sumter, Georgia is a small unincorporated rural community located in Sumter County in the southwestern part of the U.S. state of Georgia.
-
D.
Gibbes
Gibbes is a surname and variant spelling of Gibbs, historically borne by several notable figures in English-speaking countries.
-
E.
Beaufort
Beaufort is a firm, raw cow’s milk Alpine cheese from France renowned for its smooth texture and complex, nutty flavor.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sumter Triple: [Mary Cantey Sumter, familyName, Sumter]
Generated description
Sumter is a surname of English origin most notably associated with several prominent American historical figures and families.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sumter Target entity description: Sumter is a surname of English origin most notably associated with several prominent American historical figures and families.
-
A.
Sumter, South Carolina
Sumter, South Carolina is a small city in central South Carolina known for its military presence, including hosting the headquarters of United States Army Central at nearby Shaw Air Force Base.
-
B.
Palmetto
Palmetto is a long-distance Amtrak passenger train service operating along the U.S. East Coast between New York City and Savannah, Georgia.
-
C.
Sumter, Georgia
Sumter, Georgia is a small unincorporated rural community located in Sumter County in the southwestern part of the U.S. state of Georgia.
-
D.
Gibbes
Gibbes is a surname and variant spelling of Gibbs, historically borne by several notable figures in English-speaking countries.
-
E.
Beaufort
Beaufort is a firm, raw cow’s milk Alpine cheese from France renowned for its smooth texture and complex, nutty flavor.
- F. None of above. chosen
Provenance (5 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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056f387cc8190920b846995761aec |
completed | March 22, 2026, 8:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c113a65164819090883dbad3be5026 |
completed | March 23, 2026, 10:19 a.m. |
| NEDg | Description generation | batch_69c11423d05c81909298ae598c80ccb0 |
completed | March 23, 2026, 10:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c11498f2948190bcca6b8054186e75 |
completed | March 23, 2026, 10:23 a.m. |
Created at: March 22, 2026, 4:09 p.m.