Triple
T4505573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dexter's Laboratory |
E101323
|
entity |
| Predicate | composer |
P1361
|
FINISHED |
| Object |
Thomas Chase
Thomas Chase is a television and film composer best known for scoring animated series such as Dexter's Laboratory.
|
E447692
|
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: Thomas Chase | Statement: [Dexter's Laboratory, composer, Thomas Chase]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thomas Chase Context triple: [Dexter's Laboratory, composer, Thomas Chase]
-
A.
Frank Losee
Frank Losee was an American stage and silent film actor active in the early 20th century.
-
B.
Robert Florey
Robert Florey was a French-American film director and screenwriter known for his work in early Hollywood horror and experimental cinema.
-
C.
Joseph C. Wilson
Joseph C. Wilson was an American businessman best known as the pioneering leader who transformed Xerox into a major innovator in photocopying and office technology.
-
D.
Melvin Wydler
Melvin Wydler was a U.S. Congressman whose legislative work on technology and innovation policy led to a federal law being named in his honor.
-
E.
Donald F. Hornig
Donald F. Hornig was an American chemist and science advisor who served as a key presidential science counselor, notably to President Lyndon B. Johnson.
- 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: Thomas Chase Triple: [Dexter's Laboratory, composer, Thomas Chase]
Generated description
Thomas Chase is a television and film composer best known for scoring animated series such as Dexter's Laboratory.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thomas Chase Target entity description: Thomas Chase is a television and film composer best known for scoring animated series such as Dexter's Laboratory.
-
A.
Frank Losee
Frank Losee was an American stage and silent film actor active in the early 20th century.
-
B.
Robert Florey
Robert Florey was a French-American film director and screenwriter known for his work in early Hollywood horror and experimental cinema.
-
C.
Joseph C. Wilson
Joseph C. Wilson was an American businessman best known as the pioneering leader who transformed Xerox into a major innovator in photocopying and office technology.
-
D.
Melvin Wydler
Melvin Wydler was a U.S. Congressman whose legislative work on technology and innovation policy led to a federal law being named in his honor.
-
E.
Donald F. Hornig
Donald F. Hornig was an American chemist and science advisor who served as a key presidential science counselor, notably to President Lyndon B. Johnson.
- 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_69bd43d175248190894dc58b5b395c26 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56ff78748190bb667e70c69dc817 |
completed | March 20, 2026, 2:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd6f9961ac8190954b2d352d2319fb |
completed | March 20, 2026, 4:02 p.m. |
| NEDg | Description generation | batch_69bd705e0e848190a73e7ddb569f0e37 |
completed | March 20, 2026, 4:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bd71b41664819091bd4f75d4634943 |
completed | March 20, 2026, 4:11 p.m. |
Created at: March 20, 2026, 1:01 p.m.