Triple
T20819865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dominion |
E512543
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Christopher Egan |
—
|
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: Christopher Egan | Statement: [Dominion, castMember, Christopher Egan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christopher Egan Context triple: [Dominion, castMember, Christopher Egan]
-
A.
Christopher Egan
chosen
Christopher Egan is an Australian actor known for his roles in film and television, including the romantic drama "Letters to Juliet."
-
B.
Matt Durning
Matt Durning is a character from the television series "Beverly Hills, 90210," known as one of Kelly Taylor’s later love interests and a lawyer dealing with personal and professional struggles.
-
C.
Michael Pitts
Michael Pitts is a relatively common personal name shared by multiple individuals, including figures in fields such as politics, religion, and entertainment.
-
D.
Andrew Kelley
Andrew Kelley is a software engineer best known as the creator and lead developer of the Zig programming language.
-
E.
Mike Vogel
Mike Vogel is an American actor known for his roles in films like "Cloverfield" and "The Help" as well as TV series such as "Under the Dome."
- 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_69e0b4ce39108190a6e8e5df4f1c8dc5 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f6a65481909a0df78616e185e4 |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.