Triple
T8980983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bill Bolling |
E214524
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Bill Bolling |
E214524
|
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: Bill Bolling | Statement: [Bill Bolling, name, Bill Bolling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bill Bolling Context triple: [Bill Bolling, name, Bill Bolling]
-
A.
Bill Bolling
chosen
Bill Bolling is an American Republican politician who served as the 39th Lieutenant Governor of Virginia from 2006 to 2014.
-
B.
Frank Hague
Frank Hague was a powerful and controversial early 20th-century mayor of Jersey City, New Jersey, known for his political machine and involvement in landmark civil liberties cases.
-
C.
Tom Boggs
Tom Boggs is an American lawyer and lobbyist best known as the son of longtime U.S. Congressman Hale Boggs and influential Washington power broker Lindy Boggs.
-
D.
Bill Truitt
Bill Truitt is a central character in the dark comedy film "The Opposite of Sex," serving as a mild-mannered teacher whose life is upended by the manipulative actions of his teenage half-sister.
-
E.
Charles Kisseberth
Charles Kisseberth is an American linguist and phonologist known for influential work in generative phonology and coauthoring foundational texts in the field.
- 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_69ca839ea8b88190922c6a326ffcc0d3 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc67a76f748190a4abad5d53d58fa8 |
completed | April 1, 2026, 12:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d01755d26c819084c6b4967550842e |
completed | April 3, 2026, 7:39 p.m. |
Created at: March 30, 2026, 7:03 p.m.