Triple
T14075132
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meet Me in Las Vegas |
E338710
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Dan Dailey |
E188127
|
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: Dan Dailey | Statement: [Meet Me in Las Vegas, stars, Dan Dailey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dan Dailey Context triple: [Meet Me in Las Vegas, stars, Dan Dailey]
-
A.
Dan Dailey
chosen
Dan Dailey was an American actor and dancer best known for his roles in Hollywood musicals of the 1940s and 1950s.
-
B.
Bud Day
Bud Day was a highly decorated United States Air Force colonel, Medal of Honor recipient, and Vietnam War fighter pilot who became one of America’s most renowned prisoners of war.
-
C.
Paul Bolen
Paul Bolen is a musician known for being a member of the Celtic rock band O'Malley's March.
-
D.
Steve Donahue
Steve Donahue is an American college basketball coach best known for leading Cornell University to multiple NCAA Tournament appearances, including a historic Sweet Sixteen run in 2010.
-
E.
David Sills
David Sills was an American jurist and former mayor of Irvine, California, who later served as the presiding justice of the California Court of Appeal.
- 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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5c5bc49881909012b66fa451f495 |
completed | April 14, 2026, 3:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7dc62b081909f3c9259295064cf |
completed | May 7, 2026, 8:36 p.m. |
Created at: April 9, 2026, 10:21 p.m.