Triple
T13700086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Always Be My Maybe |
E328492
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Erin Westerman
Erin Westerman is a film producer known for her work on the romantic comedy "Always Be My Maybe" and other contemporary studio projects.
|
E1201634
|
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: Erin Westerman | Statement: [Always Be My Maybe, producer, Erin Westerman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erin Westerman Context triple: [Always Be My Maybe, producer, Erin Westerman]
-
A.
Erin Walton
Erin Walton is a central daughter in the Walton family on the classic American television series "The Waltons," known for her sensitivity, ambition, and evolving independence.
-
B.
Erin Richards
Erin Richards is a Welsh actress and director best known for her role as Barbara Kean in the television series "Gotham."
-
C.
Erin Burkett
Erin Burkett is an American music industry executive and co-founder of the influential punk rock record label Fat Wreck Chords.
-
D.
Erin Kelley
Erin Kelley is a lively main character known for her passion for music and her love of dancing.
-
E.
Erin Daniels
Erin Daniels is an American actress best known for her role as Dana Fairbanks on the television drama series "The L Word."
- 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: Erin Westerman Triple: [Always Be My Maybe, producer, Erin Westerman]
Generated description
Erin Westerman is a film producer known for her work on the romantic comedy "Always Be My Maybe" and other contemporary studio projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Erin Westerman Target entity description: Erin Westerman is a film producer known for her work on the romantic comedy "Always Be My Maybe" and other contemporary studio projects.
-
A.
Erin Walton
Erin Walton is a central daughter in the Walton family on the classic American television series "The Waltons," known for her sensitivity, ambition, and evolving independence.
-
B.
Erin Richards
Erin Richards is a Welsh actress and director best known for her role as Barbara Kean in the television series "Gotham."
-
C.
Erin Burkett
Erin Burkett is an American music industry executive and co-founder of the influential punk rock record label Fat Wreck Chords.
-
D.
Erin Kelley
Erin Kelley is a lively main character known for her passion for music and her love of dancing.
-
E.
Erin Daniels
Erin Daniels is an American actress best known for her role as Dana Fairbanks on the television drama series "The L Word."
- 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_6a000ebbc67c8190bd930a2773edb5b2 |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a000f7e6338819099598bc22d31cd22 |
completed | May 10, 2026, 4:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a001025300c819084933d9c6d19fe97 |
completed | May 10, 2026, 4:57 a.m. |
Created at: April 9, 2026, 9:54 p.m.