Triple
T21891939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greg Warner |
E540572
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Anthony Clark |
—
|
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: Anthony Clark | Statement: [Greg Warner, portrayedBy, Anthony Clark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anthony Clark Context triple: [Greg Warner, portrayedBy, Anthony Clark]
-
A.
Anthony Clark
chosen
Anthony Clark is an American stand-up comedian and actor best known for starring in the sitcom "Yes, Dear."
-
B.
Ian Clark
Ian Clark is the child of Jonathan Clark, known primarily in this context for his familial relationship.
-
C.
Lorenzo A. Clarkson
Lorenzo A. Clarkson was a 19th-century American businessman and philanthropist from New York, known for his involvement in finance and civic affairs.
-
D.
Curtiss Clayton
Curtiss Clayton is an American film editor known for his work on independent and art-house films, including the cult favorite "Buffalo ’66."
-
E.
Dane Clark
Dane Clark was an American film and television actor known for his tough, working-class persona in numerous 1940s and 1950s Hollywood dramas and war movies.
- 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_69e0c47a95908190ae3e19b716accb3d |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fc3727c8190b4d5d5a44aa2e55e |
completed | April 28, 2026, 8:59 p.m. |
Created at: April 16, 2026, 7:06 p.m.