Triple
T9854489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Married to the Mob |
E239549
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Mike Downey |
E323176
|
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: Mike Downey | Statement: [Married to the Mob, character, Mike Downey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Downey Context triple: [Married to the Mob, character, Mike Downey]
-
A.
Chris Downey
Chris Downey is a television writer and producer best known for co-creating the crime drama series "Leverage."
-
B.
Glen Downey
Glen Downey is a Canadian author and educator known for his work in graphic novels and educational publishing, particularly in literacy and comics-based learning.
-
C.
Brian Downey
Brian Downey is an Irish drummer best known as a founding member of the hard rock band Thin Lizzy.
-
D.
Juan Downey
Juan Downey was a Chilean video and conceptual artist known for his pioneering work in interactive video, performance, and media art that explored identity, technology, and Latin American culture.
-
E.
Michael Potts
chosen
Michael Potts is an American actor known for his work in film, television, and theater, including notable roles in projects like "The Wire," "True Detective," and various Broadway 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_69ca84e4fdc08190a624425bcef98665 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb3960fb481909c90d6d6cafc6222 |
completed | April 2, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1d5f21a04819099f23ede55ec3417 |
completed | April 5, 2026, 3:24 a.m. |
Created at: March 30, 2026, 8:34 p.m.