Triple
T136679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Mint |
E2761
|
entity |
| Predicate | formerOffice |
P1268
|
FINISHED |
| Object |
Charlotte Mint
Charlotte Mint was a 19th-century branch of the U.S. Mint in North Carolina that primarily produced gold coins from locally mined gold before the Civil War.
|
E16640
|
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: Charlotte Mint | Statement: [United States Mint, formerOffice, Charlotte Mint]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Charlotte Mint Context triple: [United States Mint, formerOffice, Charlotte Mint]
-
A.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
B.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
C.
Estelle
Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
-
D.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
E.
Angela
Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
- 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: Charlotte Mint Triple: [United States Mint, formerOffice, Charlotte Mint]
Generated description
Charlotte Mint was a 19th-century branch of the U.S. Mint in North Carolina that primarily produced gold coins from locally mined gold before the Civil War.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Charlotte Mint Target entity description: Charlotte Mint was a 19th-century branch of the U.S. Mint in North Carolina that primarily produced gold coins from locally mined gold before the Civil War.
-
A.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
B.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
-
C.
Estelle
Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
-
D.
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
E.
Angela
Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a25b82ef94819083488d3d93fdfe6f |
completed | Feb. 28, 2026, 3:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2b85fe4d481909e39b745857b62e7 |
completed | Feb. 28, 2026, 9:41 a.m. |
| NEDg | Description generation | batch_69a2b8fccfe081909f9b3d4c5f900a1c |
completed | Feb. 28, 2026, 9:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2b983d1d081909a850747695b8e0c |
completed | Feb. 28, 2026, 9:46 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.