Triple
T12946114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frank Broyles |
E309770
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Broyles |
E356969
|
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: Broyles | Statement: [Frank Broyles, familyName, Broyles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Broyles Context triple: [Frank Broyles, familyName, Broyles]
-
A.
Broyles
chosen
Broyles is a surname most notably associated with American screenwriter and Vietnam War veteran William Broyles Jr.
-
B.
Sam Bowden
Sam Bowden is the small-town lawyer protagonist in the thriller "Cape Fear," whose family is terrorized by a vengeful ex-convict he once helped imprison.
-
C.
Woody Bledsoe
Woody Bledsoe was an American mathematician and computer scientist recognized as a pioneer in artificial intelligence, particularly in automated theorem proving and pattern recognition.
-
D.
Don Galloway
Don Galloway was an American actor best known for his role as Detective Sergeant Ed Brown on the television series "Ironside."
-
E.
Tully Marshall
Tully Marshall was an American character actor of the silent and early sound film era, known for his prolific work in supporting roles across numerous Hollywood productions.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1b3694819098527dcea3cfed93 |
completed | April 10, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af75bc04819098d98c47fca48ac9 |
completed | May 3, 2026, 2:14 a.m. |
Created at: April 9, 2026, 5:43 p.m.