Triple
T16611836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robin Williams as Mork |
E403589
|
entity |
| Predicate | createdBy |
P806
|
FINISHED |
| Object | Joe Glauberg |
—
|
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: Joe Glauberg | Statement: [Robin Williams as Mork, createdBy, Joe Glauberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Joe Glauberg Context triple: [Robin Williams as Mork, createdBy, Joe Glauberg]
-
A.
Joe Glauberg
chosen
Joe Glauberg is a television writer and producer best known for his work on the hit sitcom *Mork & Mindy*.
-
B.
Joe Klotz
Joe Klotz is an American film editor best known for his acclaimed work on the drama film "Precious."
-
C.
Greg Ganske
Greg Ganske is an American plastic surgeon and Republican politician who represented Iowa in the U.S. House of Representatives from 1995 to 2003.
-
D.
Barry Kroeger
Barry Kroeger was an American character actor known for his distinctive villainous roles in mid-20th-century film and television.
-
E.
Randall Gosch
Randall Gosch is a private individual best known for having been married to actor Ted Danson before his rise to major television fame.
- 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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e36096356c819092815d64db041793 |
completed | April 18, 2026, 10:44 a.m. |
Created at: April 10, 2026, 5:17 a.m.