Triple
T15435922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. John Becker |
E369761
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object |
John Becker
John Becker is a fictional, gruff but caring Bronx doctor portrayed by Ted Danson in the American television sitcom "Becker."
|
E1219073
|
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: John Becker | Statement: [Dr. John Becker, fullName, John Becker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Becker Context triple: [Dr. John Becker, fullName, John Becker]
-
A.
Ben Becker
Ben Becker is a German actor known for his intense screen presence and roles in both film and theater.
-
B.
Louis Becker
Louis Becker is an architect recognized as a prominent protégé and collaborator of Danish architect Henning Larsen.
-
C.
Dennis Becker
Dennis Becker is a recurring character on the television series "The Rockford Files," serving as Jim Rockford’s long-suffering but loyal friend and contact within the Los Angeles Police Department.
-
D.
Glen Becker
Glen Becker is a small rural community located within the Township of South Dundas in eastern Ontario, Canada.
-
E.
Joe Becker
Joe Becker is a computer scientist best known as a co-founder of the Unicode Consortium and an early architect of the Unicode character encoding standard.
- 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: John Becker Triple: [Dr. John Becker, fullName, John Becker]
Generated description
John Becker is a fictional, gruff but caring Bronx doctor portrayed by Ted Danson in the American television sitcom "Becker."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Becker Target entity description: John Becker is a fictional, gruff but caring Bronx doctor portrayed by Ted Danson in the American television sitcom "Becker."
-
A.
Ben Becker
Ben Becker is a German actor known for his intense screen presence and roles in both film and theater.
-
B.
Louis Becker
Louis Becker is an architect recognized as a prominent protégé and collaborator of Danish architect Henning Larsen.
-
C.
Dennis Becker
Dennis Becker is a recurring character on the television series "The Rockford Files," serving as Jim Rockford’s long-suffering but loyal friend and contact within the Los Angeles Police Department.
-
D.
Glen Becker
Glen Becker is a small rural community located within the Township of South Dundas in eastern Ontario, Canada.
-
E.
Joe Becker
Joe Becker is a computer scientist best known as a co-founder of the Unicode Consortium and an early architect of the Unicode character encoding standard.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03edb3ec481908b26164d4470c9bc |
completed | April 16, 2026, 1:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00606823cc81908c461ef8764ebf41 |
completed | May 10, 2026, 10:39 a.m. |
| NEDg | Description generation | batch_6a006467da5081908a7b5a8310f9d68f |
completed | May 10, 2026, 10:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0064f76df0819097d7da878e762348 |
completed | May 10, 2026, 10:59 a.m. |
Created at: April 10, 2026, 3:21 a.m.