Triple
T5271720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Haggis |
E119272
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Diana Gettas
Diana Gettas is known as the former wife of Canadian screenwriter and director Paul Haggis.
|
E514766
|
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: Diana Gettas | Statement: [Paul Haggis, spouse, Diana Gettas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diana Gettas Context triple: [Paul Haggis, spouse, Diana Gettas]
-
A.
Diana Gould
Diana Gould was a British ballerina and actress best known for her distinguished dance career and her marriage to renowned violinist Yehudi Menuhin.
-
B.
Diana Goodman
Diana Goodman is the emotionally struggling suburban mother at the center of the rock musical "Next to Normal," whose battle with mental illness drives the show's narrative.
-
C.
Kathleen DuRoss
Kathleen DuRoss was an American former model and socialite best known as the third wife of industrialist Henry Ford II.
-
D.
Glena Goranson
Glena Goranson is the longtime wife of NFL coach Pete Carroll, known for her low public profile despite her husband’s high-profile football career.
-
E.
Virginia Weidler
Virginia Weidler was an American child actress of the 1930s and 1940s, best remembered for her witty supporting roles in classic Hollywood films such as "The Philadelphia Story."
- 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: Diana Gettas Triple: [Paul Haggis, spouse, Diana Gettas]
Generated description
Diana Gettas is known as the former wife of Canadian screenwriter and director Paul Haggis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Diana Gettas Target entity description: Diana Gettas is known as the former wife of Canadian screenwriter and director Paul Haggis.
-
A.
Diana Gould
Diana Gould was a British ballerina and actress best known for her distinguished dance career and her marriage to renowned violinist Yehudi Menuhin.
-
B.
Diana Goodman
Diana Goodman is the emotionally struggling suburban mother at the center of the rock musical "Next to Normal," whose battle with mental illness drives the show's narrative.
-
C.
Kathleen DuRoss
Kathleen DuRoss was an American former model and socialite best known as the third wife of industrialist Henry Ford II.
-
D.
Glena Goranson
Glena Goranson is the longtime wife of NFL coach Pete Carroll, known for her low public profile despite her husband’s high-profile football career.
-
E.
Virginia Weidler
Virginia Weidler was an American child actress of the 1930s and 1940s, best remembered for her witty supporting roles in classic Hollywood films such as "The Philadelphia Story."
- 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_69bd446c38e081908cdaf113bdf86790 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7c1fa01081909d589686289b624b |
completed | March 20, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf290430a08190bf4f00a558d5a1fa |
completed | March 21, 2026, 11:25 p.m. |
| NEDg | Description generation | batch_69bf29886f00819096639d721a4a5d80 |
completed | March 21, 2026, 11:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bf29e25d80819094f03f02234c357b |
completed | March 21, 2026, 11:29 p.m. |
Created at: March 20, 2026, 1:51 p.m.