Triple
T19798023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gary Beach |
E475594
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Gary Beach |
—
|
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: Gary Beach | Statement: [Gary Beach, name, Gary Beach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gary Beach Context triple: [Gary Beach, name, Gary Beach]
-
A.
Gary Beach
chosen
Gary Beach was a Tony Award–winning American actor and singer best known for his comedic stage performances, particularly in musical theatre.
-
B.
Michael Ironside
Michael Ironside is a Canadian actor known for his intense, often villainous roles in science fiction and action films such as "Total Recall," "Starship Troopers," and "Top Gun."
-
C.
Stanley Baker
Stanley Baker was a Welsh actor and film producer known for his intense screen presence and prominent roles in British cinema of the 1950s and 1960s.
-
D.
Gene Jackson
Gene Jackson is an actor known for his role in the film "Shenandoah."
-
E.
Lawrence Tierney
Lawrence Tierney was an American film and television actor best known for his tough-guy roles in classic crime films and later for his intimidating presence in movies like Quentin Tarantino's "Reservoir Dogs."
- 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653c877288190b56ee7eedea710a3 |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.