Triple
T13297598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ty Law |
E316725
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Ty Law |
E316725
|
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: Ty Law | Statement: [Ty Law, name, Ty Law]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ty Law Context triple: [Ty Law, name, Ty Law]
-
A.
Ty Law
chosen
Ty Law is a former NFL cornerback best known for his Pro Bowl career with the New England Patriots and induction into the Pro Football Hall of Fame.
-
B.
Andre Reed
Andre Reed is a former NFL wide receiver best known for his prolific career and multiple Super Bowl appearances with the Buffalo Bills in the late 1980s and 1990s.
-
C.
Michael Irvin
Michael Irvin is a Hall of Fame former NFL wide receiver best known as a key offensive star of the Dallas Cowboys dynasty of the 1990s.
-
D.
Darrell Green
Darrell Green is a Hall of Fame NFL cornerback renowned for his exceptional speed and longevity during a 20-year career with Washington’s football franchise.
-
E.
Keyshawn Johnson
Keyshawn Johnson is a former NFL wide receiver and Super Bowl champion who became a prominent sports media personality and radio host.
- 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_69d806b40ab4819094adf6c374f4811a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990a2f2708190a8f2aa7e7c0b92d2 |
completed | April 11, 2026, 12:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716dd0cd88190b0ae81b402fc31cf |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:28 p.m.