Triple
T12923322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burridge |
E309177
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Michael Burridge
Michael Burridge is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Burridge.
|
E1028460
|
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: Michael Burridge | Statement: [Burridge, hasNotableBearer, Michael Burridge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Burridge Context triple: [Burridge, hasNotableBearer, Michael Burridge]
-
A.
David Burrows
David Burrows is a film editor best known for his work on major animated features, including The Lego Movie.
-
B.
Ken Burridge
Ken Burridge is a journalist and commentator known for his work covering environmental issues, finance, and cryptocurrency.
-
C.
Ian Meadows
Ian Meadows is an Australian actor and writer known for his work in television, film, and theatre.
-
D.
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.
-
E.
Mel Hunter
Mel Hunter was an American illustrator and artist best known for his science fiction book and magazine covers in the mid-20th century.
- 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: Michael Burridge Triple: [Burridge, hasNotableBearer, Michael Burridge]
Generated description
Michael Burridge is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Burridge.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Burridge Target entity description: Michael Burridge is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Burridge.
-
A.
David Burrows
David Burrows is a film editor best known for his work on major animated features, including The Lego Movie.
-
B.
Ken Burridge
Ken Burridge is a journalist and commentator known for his work covering environmental issues, finance, and cryptocurrency.
-
C.
Ian Meadows
Ian Meadows is an Australian actor and writer known for his work in television, film, and theatre.
-
D.
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.
-
E.
Mel Hunter
Mel Hunter was an American illustrator and artist best known for his science fiction book and magazine covers in the mid-20th century.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971e9576c81908eb59569af6da877 |
completed | April 10, 2026, 9:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6fefdd3d8819091196f68c2fd5ad0 |
completed | May 3, 2026, 7:53 a.m. |
| NEDg | Description generation | batch_69f703be3d8c8190aa004eb15dfdc98e |
completed | May 3, 2026, 8:13 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f70441c874819097e91125667a41f5 |
completed | May 3, 2026, 8:16 a.m. |
Created at: April 9, 2026, 5:42 p.m.