Triple
T19474221
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | White Fang (1991 film) |
E487201
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object |
Susan Hogan
Susan Hogan is a Canadian actress known for her work in film, television, and theatre, including roles in dramas and family-oriented productions.
|
E1378592
|
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: Susan Hogan | Statement: [White Fang (1991 film), starredActor, Susan Hogan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Susan Hogan Context triple: [White Fang (1991 film), starredActor, Susan Hogan]
-
A.
Kate Hennessy
Kate Hennessy is an American writer and the granddaughter of Catholic social activist Dorothy Day, known for her memoirs and work chronicling her family’s legacy.
-
B.
Jennifer Naughton
Jennifer Naughton is a local political leader who serves as the mayor of Spring Lake, New Jersey.
-
C.
Jennifer Tighe
Jennifer Tighe is an American actress known for her work in television, film, and theater, and as the daughter of actor Kevin Tighe.
-
D.
Moira Mulroney
Moira Mulroney is a member of the Mulroney family, known for its connections to Canadian politics and entertainment.
-
E.
Lisa Sheridan
Lisa Sheridan is the troubled protagonist of the psychological thriller film "Obsessed," whose fixation drives the story's escalating tension and conflict.
- 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: Susan Hogan Triple: [White Fang (1991 film), starredActor, Susan Hogan]
Generated description
Susan Hogan is a Canadian actress known for her work in film, television, and theatre, including roles in dramas and family-oriented productions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Susan Hogan Target entity description: Susan Hogan is a Canadian actress known for her work in film, television, and theatre, including roles in dramas and family-oriented productions.
-
A.
Kate Hennessy
Kate Hennessy is an American writer and the granddaughter of Catholic social activist Dorothy Day, known for her memoirs and work chronicling her family’s legacy.
-
B.
Jennifer Naughton
Jennifer Naughton is a local political leader who serves as the mayor of Spring Lake, New Jersey.
-
C.
Jennifer Tighe
Jennifer Tighe is an American actress known for her work in television, film, and theater, and as the daughter of actor Kevin Tighe.
-
D.
Moira Mulroney
Moira Mulroney is a member of the Mulroney family, known for its connections to Canadian politics and entertainment.
-
E.
Lisa Sheridan
Lisa Sheridan is the troubled protagonist of the psychological thriller film "Obsessed," whose fixation drives the story's escalating tension and conflict.
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633ef69508190b0d71ef663ba8977 |
completed | April 20, 2026, 2:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0740519e888190863e5ca5f72ed951 |
completed | May 15, 2026, 3:48 p.m. |
| NEDg | Description generation | batch_6a07420720f881908010cdc7a140eed9 |
completed | May 15, 2026, 3:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0742f860948190a8619dce3e1461b8 |
completed | May 15, 2026, 3:59 p.m. |
Created at: April 10, 2026, 1:39 p.m.