Triple
T10823931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Boys |
E255445
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Kyle Howard
Kyle Howard is an American actor best known for his comedic roles in television series and films, including prominent parts in sitcoms.
|
E888204
|
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: Kyle Howard | Statement: [My Boys, hasCastMember, Kyle Howard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kyle Howard Context triple: [My Boys, hasCastMember, Kyle Howard]
-
A.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
-
B.
Jarin Blaschke
Jarin Blaschke is an American cinematographer best known for his stark, atmospheric black-and-white work on films like "The Lighthouse."
-
C.
Kirby Reed
Kirby Reed is a sharp-witted, horror-savvy teenager and fan-favorite character from the Scream film franchise.
-
D.
Brendan Hunt
Brendan Hunt is an American actor, writer, and comedian best known for co-creating and starring in the acclaimed television series "Ted Lasso."
-
E.
Mike Vogel
Mike Vogel is an American actor known for his roles in films like "Cloverfield" and "The Help" as well as TV series such as "Under the Dome."
- 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: Kyle Howard Triple: [My Boys, hasCastMember, Kyle Howard]
Generated description
Kyle Howard is an American actor best known for his comedic roles in television series and films, including prominent parts in sitcoms.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kyle Howard Target entity description: Kyle Howard is an American actor best known for his comedic roles in television series and films, including prominent parts in sitcoms.
-
A.
Michael Kube-McDowell
Michael Kube-McDowell is an American science fiction author known for his novels, short stories, and contributions to major franchises such as Star Wars.
-
B.
Jarin Blaschke
Jarin Blaschke is an American cinematographer best known for his stark, atmospheric black-and-white work on films like "The Lighthouse."
-
C.
Kirby Reed
Kirby Reed is a sharp-witted, horror-savvy teenager and fan-favorite character from the Scream film franchise.
-
D.
Brendan Hunt
Brendan Hunt is an American actor, writer, and comedian best known for co-creating and starring in the acclaimed television series "Ted Lasso."
-
E.
Mike Vogel
Mike Vogel is an American actor known for his roles in films like "Cloverfield" and "The Help" as well as TV series such as "Under the Dome."
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d734cf7918819094d36ea208c80d12 |
completed | April 9, 2026, 5:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de8578428c8190a25d9008881d0114 |
completed | April 14, 2026, 6:20 p.m. |
| NEDg | Description generation | batch_69de8e6f3fac8190bcd1675978d6d6d7 |
completed | April 14, 2026, 6:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69de8fa679cc81909cb51035e5403ce9 |
completed | April 14, 2026, 7:04 p.m. |
Created at: April 8, 2026, 9:19 p.m.