Triple
T14363070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hall Pass |
E356152
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Kevin Barnett
Kevin Barnett was an American comedian and screenwriter known for co-writing mainstream comedy films and working on various television and stand-up projects.
|
E1104984
|
NE FINISHED |
How this triple was built (4 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: Kevin Barnett | Statement: [Hall Pass, screenwriter, Kevin Barnett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kevin Barnett Context triple: [Hall Pass, screenwriter, Kevin Barnett]
-
A.
Kevin Burkhardt
Kevin Burkhardt is an American sportscaster best known as a play-by-play announcer and studio host for major MLB and NFL broadcasts on Fox.
-
B.
Greg Barnett
Greg Barnett is an actor known for his role in the 2013 television miniseries "The Bible."
-
C.
Greg Barnett
Greg Barnett is a guitarist and vocalist best known as a member of the American punk rock band The Menzingers.
-
D.
Michael Barnett
Michael Barnett is an international relations scholar known for his work on global governance, humanitarianism, and international organizations.
-
E.
Kevin Harkey
Kevin Harkey is an American animator and storyboard artist best known for his story work on Disney animated features, including the 1991 classic "Beauty and the Beast."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kevin Barnett Triple: [Hall Pass, screenwriter, Kevin Barnett]
Generated description
Kevin Barnett was an American comedian and screenwriter known for co-writing mainstream comedy films and working on various television and stand-up projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kevin Barnett Target entity description: Kevin Barnett was an American comedian and screenwriter known for co-writing mainstream comedy films and working on various television and stand-up projects.
-
A.
Kevin Burkhardt
Kevin Burkhardt is an American sportscaster best known as a play-by-play announcer and studio host for major MLB and NFL broadcasts on Fox.
-
B.
Greg Barnett
Greg Barnett is an actor known for his role in the 2013 television miniseries "The Bible."
-
C.
Greg Barnett
Greg Barnett is a guitarist and vocalist best known as a member of the American punk rock band The Menzingers.
-
D.
Michael Barnett
Michael Barnett is an international relations scholar known for his work on global governance, humanitarianism, and international organizations.
-
E.
Kevin Harkey
Kevin Harkey is an American animator and storyboard artist best known for his story work on Disney animated features, including the 1991 classic "Beauty and the Beast."
- F. None of above. chosen
Provenance (5 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fabec088190bd8128371b29e958 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd7a3479088190929ab4b9d218a608 |
completed | May 8, 2026, 5:52 a.m. |
| NEDg | Description generation | batch_69fd7d80d358819095661dac316ff69f |
completed | May 8, 2026, 6:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd7e0a30188190942ff45e0f865490 |
completed | May 8, 2026, 6:09 a.m. |
Created at: April 10, 2026, 1:15 a.m.