Triple
T7249149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunsmoke |
E156550
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Ken Curtis |
E423993
|
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: Ken Curtis | Statement: [Gunsmoke, stars, Ken Curtis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ken Curtis Context triple: [Gunsmoke, stars, Ken Curtis]
-
A.
Ken Curtis
chosen
Ken Curtis was an American actor and singer best known for playing Festus Haggen on the long-running television western "Gunsmoke."
-
B.
Don Cannon
Don Cannon is an American hip-hop record producer and DJ known for his influential work with artists like Jeezy, Lil Uzi Vert, and others in the Atlanta rap scene.
-
C.
Glen Tullman
Glen Tullman is an American healthcare technology entrepreneur and executive best known for leading and building major digital health companies, including Allscripts.
-
D.
Bob McElwee
Bob McElwee is a former National Football League official best known for serving as the referee in multiple Super Bowls and for his long, distinguished career in professional football officiating.
-
E.
Larry Russell
Larry Russell was an American composer best known for his Academy Award-winning work in film music.
- 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_69c68827b5e481908dc05e145b2c92d4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea77a3588190accf31860170f052 |
completed | March 27, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7d3a2ba748190890c2980ca4a8ecd |
completed | March 28, 2026, 1:12 p.m. |
Created at: March 27, 2026, 2:56 p.m.