Triple
T10977327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie Brent |
E259404
|
entity |
| Predicate | stepfather |
P6826
|
FINISHED |
| Object |
Phil Brent
Phil Brent is a fictional character from the soap opera "All My Children," known for his complex family relationships and dramatic storylines.
|
E994122
|
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: Phil Brent | Statement: [Charlie Brent, stepfather, Phil Brent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Phil Brent Context triple: [Charlie Brent, stepfather, Phil Brent]
-
A.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
B.
Greg Barnett
Greg Barnett is an actor known for his role in the 2013 television miniseries "The Bible."
-
C.
Mike Burrows
Mike Burrows is a computer scientist best known for his influential work at Google on large-scale distributed systems, including co-authoring the Bigtable storage system.
-
D.
Mike Dailey
Mike Dailey is an American arena football coach best known for leading the Albany Firebirds and later the Colorado Crush to success in the Arena Football League.
-
E.
Phil Stong
Phil Stong was an American novelist and journalist best known for his 1932 novel "State Fair," which inspired multiple film adaptations.
- 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: Phil Brent Triple: [Charlie Brent, stepfather, Phil Brent]
Generated description
Phil Brent is a fictional character from the soap opera "All My Children," known for his complex family relationships and dramatic storylines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Phil Brent Target entity description: Phil Brent is a fictional character from the soap opera "All My Children," known for his complex family relationships and dramatic storylines.
-
A.
Phil DeVoss
Phil DeVoss is a fictional character from the romantic comedy-drama film "Elizabethtown," which explores themes of family, failure, and self-discovery.
-
B.
Greg Barnett
Greg Barnett is an actor known for his role in the 2013 television miniseries "The Bible."
-
C.
Mike Burrows
Mike Burrows is a computer scientist best known for his influential work at Google on large-scale distributed systems, including co-authoring the Bigtable storage system.
-
D.
Mike Dailey
Mike Dailey is an American arena football coach best known for leading the Albany Firebirds and later the Colorado Crush to success in the Arena Football League.
-
E.
Phil Stong
Phil Stong was an American novelist and journalist best known for his 1932 novel "State Fair," which inspired multiple film adaptations.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d771f6a9448190b3932ee801ae0da9 |
completed | April 9, 2026, 9:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6684574908190bd7e3d1a7dd6d876 |
completed | May 2, 2026, 9:10 p.m. |
| NEDg | Description generation | batch_69f669527fe881909baeb84ccff506c8 |
completed | May 2, 2026, 9:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f669fe4bc48190adba50ad58b10c45 |
completed | May 2, 2026, 9:17 p.m. |
Created at: April 8, 2026, 9:24 p.m.