Triple
T21899915
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Client (TV series) |
E540780
|
entity |
| Predicate | mainCastMember |
P5563
|
FINISHED |
| Object | David Barry Gray |
—
|
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: David Barry Gray | Statement: [The Client (TV series), mainCastMember, David Barry Gray]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Barry Gray Context triple: [The Client (TV series), mainCastMember, David Barry Gray]
-
A.
David Barry Gray
chosen
David Barry Gray is an American actor known for his roles in film and television, including a part in the 1997 drama "Lawn Dogs."
-
B.
Tony Gray
Tony Gray, also known by his gamer tag "Zikz," is a professional esports coach best known for his work in competitive League of Legends.
-
C.
Daniel S. Gray
Daniel S. Gray was a 19th-century businessman and early settler best known for establishing the community that became Montgomery, Illinois.
-
D.
Alexander Gray
Alexander Gray was a Scottish civil servant, economist, and poet known for his translations of German and Danish poetry and his contributions to public finance.
-
E.
R. A. Gray
R. A. Gray was a long-serving Florida Secretary of State and historian known for his contributions to the preservation and documentation of Florida’s history.
- 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_69e0c47b4e8c81908c8076eaa4c8e4f2 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f11fca2bf88190b2a5b912aa102513 |
completed | April 28, 2026, 8:59 p.m. |
Created at: April 16, 2026, 7:07 p.m.