Triple
T13700016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lake House |
E328490
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Kate Forster
Kate Forster is the lonely Chicago doctor who forms a mysterious, time-crossed romance through letters in the film "The Lake House."
|
E1056086
|
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: Kate Forster | Statement: [The Lake House, mainCharacter, Kate Forster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Forster Context triple: [The Lake House, mainCharacter, Kate Forster]
-
A.
Emma Forrest
Emma Forrest is a British-born novelist, journalist, and screenwriter known for her memoir "Your Voice in My Head" and her work in both literature and film.
-
B.
Charlotte Forsyth
Charlotte Forsyth is a daughter of the late British television entertainer and presenter Sir Bruce Forsyth.
-
C.
Anne Forster
Anne Forster was the wife of the Irish philosopher and Anglican bishop George Berkeley, known primarily through her marriage into his prominent intellectual and clerical household.
-
D.
Evie Forster
Evie Forster is known as the wife of American actor Robert Forster.
-
E.
Kate Forte
Kate Forte is a film and television producer best known for her work on projects such as the drama film "The Great Debaters."
- 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: Kate Forster Triple: [The Lake House, mainCharacter, Kate Forster]
Generated description
Kate Forster is the lonely Chicago doctor who forms a mysterious, time-crossed romance through letters in the film "The Lake House."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kate Forster Target entity description: Kate Forster is the lonely Chicago doctor who forms a mysterious, time-crossed romance through letters in the film "The Lake House."
-
A.
Emma Forrest
Emma Forrest is a British-born novelist, journalist, and screenwriter known for her memoir "Your Voice in My Head" and her work in both literature and film.
-
B.
Charlotte Forsyth
Charlotte Forsyth is a daughter of the late British television entertainer and presenter Sir Bruce Forsyth.
-
C.
Anne Forster
Anne Forster was the wife of the Irish philosopher and Anglican bishop George Berkeley, known primarily through her marriage into his prominent intellectual and clerical household.
-
D.
Evie Forster
Evie Forster is known as the wife of American actor Robert Forster.
-
E.
Kate Forte
Kate Forte is a film and television producer best known for her work on projects such as the drama film "The Great Debaters."
- 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_69d8076ff62081908a7bd79889edd7a0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc879adc88190b03f1cf815b71061 |
completed | April 12, 2026, 4:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f794559e9c81909ef8a6d9b9f480b3 |
completed | May 3, 2026, 6:30 p.m. |
| NEDg | Description generation | batch_69f798aab9c48190acaa78864e89411f |
completed | May 3, 2026, 6:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f79943e4f4819098fa82cb6a32e08a |
completed | May 3, 2026, 6:51 p.m. |
Created at: April 9, 2026, 9:54 p.m.