Triple
T13558953
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arliss |
E323853
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Michael Boatman |
E699909
|
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: Michael Boatman | Statement: [Arliss, starring, Michael Boatman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Boatman Context triple: [Arliss, starring, Michael Boatman]
-
A.
Michael Boatman
chosen
Michael Boatman is an American actor and writer best known for his comedic and dramatic television roles in series such as Spin City and The Good Wife.
-
B.
Colin Morgan
Colin Morgan is a Northern Irish actor best known for his title role in the BBC fantasy series "Merlin" and various acclaimed stage and screen performances.
-
C.
Angus Cloud
Angus Cloud was an American actor best known for his breakout role as the lovable drug dealer Fezco on HBO's teen drama series "Euphoria."
-
D.
Caleb Landry Jones
Caleb Landry Jones is an American actor and musician known for his intense, often unsettling performances in films such as "Get Out," "Three Billboards Outside Ebbing, Missouri," and "Nitram."
-
E.
Jaime King
Jaime King is an American actress and former fashion model known for her roles in films like "Sin City" and the television series "Hart of Dixie."
- 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_69d8076830b48190910a902bae5888e2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaff4223c8190801d153ae8f94c73 |
completed | April 12, 2026, 2:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75dab4974819097880ad4d50f1b34 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:47 p.m.