Triple
T23210043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wos |
E580566
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Larry Wos |
—
|
NE NERFINISHED |
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: Larry Wos | Statement: [Wos, hasNotableBearer, Larry Wos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Larry Wos Context triple: [Wos, hasNotableBearer, Larry Wos]
-
A.
Larry Wos
chosen
Larry Wos was a pioneering American mathematician and computer scientist known for his groundbreaking work in automated theorem proving.
-
B.
Larry Winters
Larry Winters is a fictional character featured in the horror film "Silent Scream."
-
C.
Larry DeWaay
Larry DeWaay is a film producer best known for his work on the action war film "The Dogs of War."
-
D.
Bill Wittliff
Bill Wittliff was an American screenwriter, author, and photographer best known for adapting and writing acclaimed Western-themed films and television miniseries.
-
E.
Larry Seiple
Larry Seiple is a former American football player best known as the versatile punter and occasional offensive contributor for the Miami Dolphins during their dominant early-1970s era.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24602ae1481908aaa6bc7ca493867 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f191609c64819096ace0d286d36f76 |
completed | April 29, 2026, 5:04 a.m. |
Created at: April 17, 2026, 4:07 p.m.