Triple
T7979840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Sarbanes |
E185542
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
John
John Sarbanes is an American politician and attorney who has served as a Democratic member of the U.S. House of Representatives from Maryland.
|
E702083
|
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: John | Statement: [John Sarbanes, givenName, John]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Context triple: [John Sarbanes, givenName, John]
-
A.
John
John is the given first name of J. Edgar Hoover, the long-serving and influential first director of the United States Federal Bureau of Investigation (FBI).
-
B.
John
John is the given name of the late American comedian and actor John Belushi, famed for his work on "Saturday Night Live" and in films like "Animal House" and "The Blues Brothers."
-
C.
John
John is the given name of the influential American jazz saxophonist and composer John Coltrane.
-
D.
John
John is the given name of John Stevens Henslow, the 19th-century English clergyman, botanist, and mentor to Charles Darwin.
-
E.
John
John is the birth name of American character actor Jack Warden, known for his prolific film and television career in the mid-20th century.
- 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: John Triple: [John Sarbanes, givenName, John]
Generated description
John Sarbanes is an American politician and attorney who has served as a Democratic member of the U.S. House of Representatives from Maryland.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Target entity description: John Sarbanes is an American politician and attorney who has served as a Democratic member of the U.S. House of Representatives from Maryland.
-
A.
John
John E. Sununu is an American politician and engineer who served as a U.S. Representative and U.S. Senator from New Hampshire.
-
B.
John
John is the first name of Dennis Hastert, the former Speaker of the United States House of Representatives.
-
C.
John
John Fetterman is an American politician serving as the junior United States senator from Pennsylvania and former lieutenant governor of the state.
-
D.
John
John is the given name of John Edward Fogarty, an American politician who served as a U.S. Representative from Rhode Island.
-
E.
John
John Lewis was a prominent American civil rights leader and long-serving U.S. Congressman known for his key role in the struggle for racial equality.
- 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_69ca829851908190b4e03829353ee7c3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c261904819086910898071f3629 |
completed | March 31, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbdef32d4c8190a2e5c76d2db6c45f |
completed | March 31, 2026, 2:49 p.m. |
| NEDg | Description generation | batch_69cbe43e47048190a0044477f88de5d0 |
completed | March 31, 2026, 3:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cc0d60254c819087d1de7ca6ea554b |
completed | March 31, 2026, 6:07 p.m. |
Created at: March 30, 2026, 5:14 p.m.