Triple
T6185539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abner Nash |
E138045
|
entity |
| Predicate | succeededBy |
P78
|
FINISHED |
| Object | Thomas Burke |
E236787
|
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: Thomas Burke | Statement: [Abner Nash, succeededBy, Thomas Burke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas Burke Context triple: [Abner Nash, succeededBy, Thomas Burke]
-
A.
Thomas Burke
Thomas Burke was an American sprinter who became the first Olympic champion in both the 100-meter and 400-meter races at the modern Games.
-
B.
Thomas Burke
chosen
Thomas Burke was an American politician who served as the third Governor of North Carolina during the early years of the United States.
-
C.
Thomas Burke
Thomas Burke was a British author best known for his early 20th-century stories set in London’s East End, including the tale that inspired the film "Broken Blossoms."
-
D.
George Barr McCutcheon
George Barr McCutcheon was an American novelist best known for his popular early 20th-century works such as the novel "Brewster's Millions," which inspired numerous film adaptations.
-
E.
James Sansbury
James Sansbury is a technology entrepreneur best known as a co-founder of the software company Altera.
- 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_69c008a8fd408190b7ec6e42934974a6 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062150fc48190877240abe6b6c636 |
completed | March 22, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c141ca916c8190bd1ca46f2b8c9c18 |
completed | March 23, 2026, 1:36 p.m. |
Created at: March 22, 2026, 4:19 p.m.