Triple
T15219303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerry Juhl |
E363719
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Jerry Juhl |
E363719
|
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: Jerry Juhl | Statement: [Jerry Juhl, name, Jerry Juhl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jerry Juhl Context triple: [Jerry Juhl, name, Jerry Juhl]
-
A.
Jerry Juhl
chosen
Jerry Juhl was an American screenwriter best known as the head writer for Jim Henson’s Muppets, contributing to projects like The Muppet Show and several Muppet films.
-
B.
Duane Schuler
Duane Schuler is an American theatrical lighting designer known for his work in opera, including major productions at leading opera houses.
-
C.
Dan Pfeiffer
Dan Pfeiffer is an American political strategist and former White House communications director who served as a senior adviser to President Barack Obama.
-
D.
John Diehl
John Diehl is an American character actor best known for his role as Detective Larry Zito on the 1980s television series "Miami Vice."
-
E.
Fred Schuler
Fred Schuler is a cinematographer best known for his work on films such as the 1980 comedy "Stir Crazy."
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007709d3881908384f0fe1e0218d0 |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff908410548190ada5d4f71d52919b |
completed | May 9, 2026, 7:52 p.m. |
Created at: April 10, 2026, 3:11 a.m.