Triple
T4559397
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Newsroom |
E120555
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Thomas Sadoski |
E230781
|
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: Thomas Sadoski | Statement: [The Newsroom, portrayedBy, Thomas Sadoski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas Sadoski Context triple: [The Newsroom, portrayedBy, Thomas Sadoski]
-
A.
Thomas Sadoski
chosen
Thomas Sadoski is an American actor known for his roles in television series like "The Newsroom" and films such as "John Wick" and "Wild."
-
B.
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."
-
C.
Bob Gunton
Bob Gunton is an American character actor best known for his portrayal of the strict prison warden Samuel Norton in the film "The Shawshank Redemption."
-
D.
Bill Hartnett
Bill Hartnett is a person notable enough to be recognized as a bearer of the surname Hartnett, though specific widely known achievements or roles are not clearly documented.
-
E.
Michael Pitts
Michael Pitts is a relatively common personal name shared by multiple individuals, including figures in fields such as politics, religion, and entertainment.
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd582b871c8190be0b70c76d639000 |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdd3a139f08190a0211d5848ccdfad |
completed | March 20, 2026, 11:09 p.m. |
Created at: March 20, 2026, 1:09 p.m.