Triple
T20819866
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dominion |
E512543
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Tom Wisdom |
—
|
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: Tom Wisdom | Statement: [Dominion, castMember, Tom Wisdom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Wisdom Context triple: [Dominion, castMember, Tom Wisdom]
-
A.
Tom Wisdom
chosen
Tom Wisdom is a British actor known for his roles in films like "300" and various television dramas.
-
B.
Michael Wisdom
Michael Wisdom is an individual notable enough to be recognized as a bearer of the surname Wisdom, though specific widely known public details about him are not clearly established.
-
C.
Olli Wisdom
Olli Wisdom was a British musician and DJ best known as a pioneering figure in the psychedelic trance scene and frontman of the gothic rock band Specimen.
-
D.
Wes Wise
Wes Wise is an American journalist and politician who served as mayor of Dallas, Texas, in the 1970s.
-
E.
Tom Latham
Tom Latham is a Republican politician who served as a U.S. Representative from Iowa.
- 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.