Triple
T1072673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Dryden |
E23363
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Lynda Dryden
Lynda Dryden is the wife of former NHL goaltender and Canadian politician Ken Dryden.
|
E238354
|
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: Lynda Dryden | Statement: [Ken Dryden, spouse, Lynda Dryden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lynda Dryden Context triple: [Ken Dryden, spouse, Lynda Dryden]
-
A.
Kate Garvey
Kate Garvey is a British public relations executive and former political aide, known for her work with Tony Blair and her marriage to Wikipedia co-founder Jimmy Wales.
-
B.
Melissa Mathison
Melissa Mathison was an American screenwriter best known for writing the screenplay for Steven Spielberg’s film "E.T. the Extra-Terrestrial."
-
C.
Lisa Harrow
Lisa Harrow is a New Zealand-born actress known for her work in film, television, and theatre, including prominent roles in Australian and British productions.
-
D.
Maryann Brandon
Maryann Brandon is a film editor best known for her work on major Hollywood blockbusters, including J.J. Abrams–directed projects such as Star Trek (2009) and several Star Wars films.
-
E.
Amy Landecker
Amy Landecker is an American actress best known for her role as Sarah Pfefferman on the television series "Transparent."
- 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: Lynda Dryden Triple: [Ken Dryden, spouse, Lynda Dryden]
Generated description
Lynda Dryden is the wife of former NHL goaltender and Canadian politician Ken Dryden.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lynda Dryden Target entity description: Lynda Dryden is the wife of former NHL goaltender and Canadian politician Ken Dryden.
-
A.
Kate Garvey
Kate Garvey is a British public relations executive and former political aide, known for her work with Tony Blair and her marriage to Wikipedia co-founder Jimmy Wales.
-
B.
Melissa Mathison
Melissa Mathison was an American screenwriter best known for writing the screenplay for Steven Spielberg’s film "E.T. the Extra-Terrestrial."
-
C.
Lisa Harrow
Lisa Harrow is a New Zealand-born actress known for her work in film, television, and theatre, including prominent roles in Australian and British productions.
-
D.
Maryann Brandon
Maryann Brandon is a film editor best known for her work on major Hollywood blockbusters, including J.J. Abrams–directed projects such as Star Trek (2009) and several Star Wars films.
-
E.
Amy Landecker
Amy Landecker is an American actress best known for her role as Sarah Pfefferman on the television series "Transparent."
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b92afad88190b7705923f71fc760 |
completed | March 1, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae589f5c588190a207ffa2691490b7 |
completed | March 9, 2026, 5:20 a.m. |
| NEDg | Description generation | batch_69ae59892b848190a9cc8b086647ff14 |
completed | March 9, 2026, 5:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae59ff1234819083bbfdce270cb584 |
completed | March 9, 2026, 5:26 a.m. |
Created at: March 1, 2026, 7:42 p.m.