Triple
T12462900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SFU Red Leafs |
E297841
|
entity |
| Predicate | athleticDirector |
P745
|
FINISHED |
| Object |
Theresa Hanson
Theresa Hanson is a Canadian university sports administrator who serves as the athletic director for Simon Fraser University's Red Leafs athletic program.
|
E995678
|
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: Theresa Hanson | Statement: [SFU Red Leafs, athleticDirector, Theresa Hanson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Theresa Hanson Context triple: [SFU Red Leafs, athleticDirector, Theresa Hanson]
-
A.
Theresa Randle
Theresa Randle is an American actress known for her roles in films such as "Bad Boys," "Spawn," and "Girl 6."
-
B.
Theresa Eichenwald
Theresa Eichenwald is an American physician and academic known for her work in pediatrics and infectious diseases.
-
C.
Theresa Galloway
Theresa Galloway is known as the wife of American war correspondent and author Joseph L. Galloway.
-
D.
Theresa Russell
Theresa Russell is an American actress known for her intense and often provocative performances in films such as "Bad Timing," "Black Widow," and "Track 29."
-
E.
Theresa Preston-Werner
Theresa Preston-Werner is an American entrepreneur and co-founder of the prenatal health startup Oula, known for her work at the intersection of technology and maternal healthcare.
- 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: Theresa Hanson Triple: [SFU Red Leafs, athleticDirector, Theresa Hanson]
Generated description
Theresa Hanson is a Canadian university sports administrator who serves as the athletic director for Simon Fraser University's Red Leafs athletic program.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Theresa Hanson Target entity description: Theresa Hanson is a Canadian university sports administrator who serves as the athletic director for Simon Fraser University's Red Leafs athletic program.
-
A.
Theresa Randle
Theresa Randle is an American actress known for her roles in films such as "Bad Boys," "Spawn," and "Girl 6."
-
B.
Theresa Eichenwald
Theresa Eichenwald is an American physician and academic known for her work in pediatrics and infectious diseases.
-
C.
Theresa Galloway
Theresa Galloway is known as the wife of American war correspondent and author Joseph L. Galloway.
-
D.
Theresa Russell
Theresa Russell is an American actress known for her intense and often provocative performances in films such as "Bad Timing," "Black Widow," and "Track 29."
-
E.
Theresa Preston-Werner
Theresa Preston-Werner is an American entrepreneur and co-founder of the prenatal health startup Oula, known for her work at the intersection of technology and maternal healthcare.
- 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_69d6ada270808190b1a2b2e7b02bb426 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94db5efe88190a76949e4ddc3314c |
completed | April 10, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f66859c0448190980c5e490cc41118 |
completed | May 2, 2026, 9:10 p.m. |
| NEDg | Description generation | batch_69f669c9454081909d39d5bb7082fb00 |
completed | May 2, 2026, 9:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f66b64dfe08190a7f9283dabd0e3c7 |
completed | May 2, 2026, 9:23 p.m. |
Created at: April 8, 2026, 9:56 p.m.