Triple
T21960865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Judd Garrett |
E542322
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object | John Garrett |
—
|
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: John Garrett | Statement: [Judd Garrett, hasSibling, John Garrett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Garrett Context triple: [Judd Garrett, hasSibling, John Garrett]
-
A.
John Garrett (American football coach)
chosen
John Garrett is an American football coach and former player who has held various assistant and coordinator roles in college and the NFL, and is the brother of former Dallas Cowboys head coach Jason Garrett.
-
B.
Dan Tucker
Dan Tucker is the titular, comical protagonist of the 19th-century American minstrel song "Old Dan Tucker," often depicted as a boisterous, rustic figure.
-
C.
Bill Romo
Bill Romo is an individual notable for sharing the surname Romo, which is associated with several public figures in sports and entertainment.
-
D.
Tom Burleson
Tom Burleson is a retired American professional basketball center best known for his shot-blocking and rebounding in the NBA during the 1970s.
-
E.
Scott Weinberger
Scott Weinberger is a media producer and former law enforcement professional best known for creating and producing true-crime television and podcast content.
- 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_69e0c47fab1081908dc74a6545dbb051 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12455e99c819092ec59fe571f814e |
completed | April 28, 2026, 9:19 p.m. |
Created at: April 16, 2026, 8 p.m.