Triple
T5638002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toronto Rock |
E124195
|
entity |
| Predicate | headCoach |
P256
|
FINISHED |
| Object |
Matt Sawyer
Matt Sawyer is a Canadian lacrosse coach best known for leading the Toronto Rock in the National Lacrosse League.
|
E542369
|
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: Matt Sawyer | Statement: [Toronto Rock, headCoach, Matt Sawyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Sawyer Context triple: [Toronto Rock, headCoach, Matt Sawyer]
-
A.
Matt Swanson
Matt Swanson is an entrepreneur best known as a co-founder of the short-form video creation and sharing platform MixBit.
-
B.
Joe Dougherty
Joe Dougherty was an American voice actor best known for originating the voice of the Warner Bros. cartoon character Porky Pig in the 1930s.
-
C.
Matthew Holworthy
Matthew Holworthy was a 17th-century English merchant and philanthropist best known for endowing the Holworthy Professorship of English Law at the University of Cambridge.
-
D.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
E.
Michael Potts
Michael Potts is an American actor known for his work in film, television, and theater, including notable roles in projects like "The Wire," "True Detective," and various Broadway productions.
- 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: Matt Sawyer Triple: [Toronto Rock, headCoach, Matt Sawyer]
Generated description
Matt Sawyer is a Canadian lacrosse coach best known for leading the Toronto Rock in the National Lacrosse League.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matt Sawyer Target entity description: Matt Sawyer is a Canadian lacrosse coach best known for leading the Toronto Rock in the National Lacrosse League.
-
A.
Matt Swanson
Matt Swanson is an entrepreneur best known as a co-founder of the short-form video creation and sharing platform MixBit.
-
B.
Joe Dougherty
Joe Dougherty was an American voice actor best known for originating the voice of the Warner Bros. cartoon character Porky Pig in the 1930s.
-
C.
Matthew Holworthy
Matthew Holworthy was a 17th-century English merchant and philanthropist best known for endowing the Holworthy Professorship of English Law at the University of Cambridge.
-
D.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
E.
Michael Potts
Michael Potts is an American actor known for his work in film, television, and theater, including notable roles in projects like "The Wire," "True Detective," and various Broadway productions.
- 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_69c00824643c81909ffdb888a2d35189 |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022820b7c81908f79c0a124d6b940 |
completed | March 22, 2026, 5:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a1a14208190a0934d7c6cf0fd5e |
completed | March 22, 2026, 9:07 p.m. |
| NEDg | Description generation | batch_69c05e02edc48190938613946f19df01 |
completed | March 22, 2026, 9:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c06209c3588190a6ededf9c198d5c5 |
completed | March 22, 2026, 9:41 p.m. |
Created at: March 22, 2026, 3:41 p.m.