Triple
T20005431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irving Bailiff |
E494445
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object | Dan Erickson |
—
|
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: Dan Erickson | Statement: [Irving Bailiff, creator, Dan Erickson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Erickson Context triple: [Irving Bailiff, creator, Dan Erickson]
-
A.
Dan Erickson
chosen
Dan Erickson is a television writer and producer best known for creating the acclaimed sci-fi thriller series "Severance."
-
B.
Christopher Sweeney
Christopher Sweeney is a music video director known for his work on high-profile pop and alternative artists’ videos, including Lily Allen’s “Hard Out Here.”
-
C.
Keith Erickson
Keith Erickson is a former American professional basketball player best known as a versatile guard-forward in the NBA during the 1960s and 1970s, including championship runs with the Los Angeles Lakers.
-
D.
Josh Gudwin
Josh Gudwin is a Grammy-winning Canadian recording and mixing engineer best known for his work with major pop artists such as Justin Bieber.
-
E.
Ryan Haddon
Ryan Haddon is an American journalist, television producer, and presenter known for her work on various entertainment and news programs.
- 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_69da626b2d748190886981ea90c8b2ea |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e661a46c748190a141ab5aac6ea250 |
completed | April 20, 2026, 5:25 p.m. |
Created at: April 11, 2026, 3:33 p.m.