Triple
T15261962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Todd Field |
E364798
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Serena Rathbun
Serena Rathbun is an American costume designer known for her work on films such as "Little Children" and "In the Bedroom."
|
E1146642
|
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: Serena Rathbun | Statement: [Todd Field, spouse, Serena Rathbun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serena Rathbun Context triple: [Todd Field, spouse, Serena Rathbun]
-
A.
Serena Benson
Serena Benson is the late mother of NYPD Captain Olivia Benson in the television series "Law & Order: Special Victims Unit," whose traumatic history and alcoholism deeply influenced Olivia's life.
-
B.
Serena Evans
Serena Evans is a British actress best known for her role in the BBC sitcom "The Thin Blue Line."
-
C.
Serena Brown
Serena Brown is the daughter of Bob Brown.
-
D.
Diana Rathbun
Diana Rathbun is a film producer best known for her work on the epic historical war movie "Troy."
-
E.
Serena Southerlyn
Serena Southerlyn is a fictional Assistant District Attorney on the long-running television series "Law & Order," known for her idealism and strong moral convictions in prosecuting cases.
- 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: Serena Rathbun Triple: [Todd Field, spouse, Serena Rathbun]
Generated description
Serena Rathbun is an American costume designer known for her work on films such as "Little Children" and "In the Bedroom."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Serena Rathbun Target entity description: Serena Rathbun is an American costume designer known for her work on films such as "Little Children" and "In the Bedroom."
-
A.
Serena Benson
Serena Benson is the late mother of NYPD Captain Olivia Benson in the television series "Law & Order: Special Victims Unit," whose traumatic history and alcoholism deeply influenced Olivia's life.
-
B.
Serena Evans
Serena Evans is a British actress best known for her role in the BBC sitcom "The Thin Blue Line."
-
C.
Serena Brown
Serena Brown is the daughter of Bob Brown.
-
D.
Diana Rathbun
Diana Rathbun is a film producer best known for her work on the epic historical war movie "Troy."
-
E.
Serena Southerlyn
Serena Southerlyn is a fictional Assistant District Attorney on the long-running television series "Law & Order," known for her idealism and strong moral convictions in prosecuting cases.
- 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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0084e85a08190b8e63598b9f6a535 |
completed | April 15, 2026, 9:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fee5fb8b30819096d31ba5884715c9 |
completed | May 9, 2026, 7:44 a.m. |
| NEDg | Description generation | batch_69fee805f5bc8190a6095e3c374f3441 |
completed | May 9, 2026, 7:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fee8e4dc688190bd597f8d710c8afc |
completed | May 9, 2026, 7:57 a.m. |
Created at: April 10, 2026, 3:14 a.m.