Triple
T488504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Snyder |
E9932
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Tanya Snyder
Tanya Snyder is an American businesswoman and philanthropist best known as the co-owner and former co-CEO of the Washington Commanders NFL franchise.
|
E187141
|
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: Tanya Snyder | Statement: [Dan Snyder, spouse, Tanya Snyder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tanya Snyder Context triple: [Dan Snyder, spouse, Tanya Snyder]
-
A.
Colleen Sostorics
Colleen Sostorics is a Canadian former ice hockey defenceman and three-time Olympic gold medallist who starred with the national women’s team.
-
B.
Melinda Rogers
Melinda Rogers is a Canadian business executive and member of the Rogers family, known for her leadership roles within Rogers Communications.
-
C.
Nancy Shevell
Nancy Shevell is an American businesswoman and heiress best known for her long-term relationship and marriage to musician Paul McCartney.
-
D.
Laura Jarrett
Laura Jarrett is an American attorney and journalist known for her work as a legal correspondent on major U.S. news networks.
-
E.
Emily Drinkard
Emily Drinkard, better known as Cissy Houston, is an American soul and gospel singer and the mother of Whitney Houston.
- 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: Tanya Snyder Triple: [Dan Snyder, spouse, Tanya Snyder]
Generated description
Tanya Snyder is an American businesswoman and philanthropist best known as the co-owner and former co-CEO of the Washington Commanders NFL franchise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tanya Snyder Target entity description: Tanya Snyder is an American businesswoman and philanthropist best known as the co-owner and former co-CEO of the Washington Commanders NFL franchise.
-
A.
Colleen Sostorics
Colleen Sostorics is a Canadian former ice hockey defenceman and three-time Olympic gold medallist who starred with the national women’s team.
-
B.
Melinda Rogers
Melinda Rogers is a Canadian business executive and member of the Rogers family, known for her leadership roles within Rogers Communications.
-
C.
Nancy Shevell
Nancy Shevell is an American businesswoman and heiress best known for her long-term relationship and marriage to musician Paul McCartney.
-
D.
Laura Jarrett
Laura Jarrett is an American attorney and journalist known for her work as a legal correspondent on major U.S. news networks.
-
E.
Emily Drinkard
Emily Drinkard, better known as Cissy Houston, is an American soul and gospel singer and the mother of Whitney Houston.
- 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_69a2e802e2908190ab17c9479e0b6412 |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0df764481909811d9483dfbc4aa |
completed | Feb. 28, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad67e82a2481909f6d2737f820121a |
completed | March 8, 2026, 12:13 p.m. |
| NEDg | Description generation | batch_69ad68895550819080fdc84d77201e60 |
completed | March 8, 2026, 12:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad6902cb3c8190ba36dc74e1dccb26 |
completed | March 8, 2026, 12:18 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.