Triple
T23152873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mouth to Mouth |
E578365
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Beatrice Brown
Beatrice Brown is an actress known for her role in the film "Mouth to Mouth."
|
E1574807
|
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: Beatrice Brown | Statement: [Mouth to Mouth, starring, Beatrice Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beatrice Brown Context triple: [Mouth to Mouth, starring, Beatrice Brown]
-
A.
Beatrice Pearson
Beatrice Pearson was an American film actress best known for her leading role in the 1948 film noir "Force of Evil."
-
B.
Beatrice Weeks
Beatrice Weeks was the second wife of iconic horror film actor Bela Lugosi, briefly married to him in the early 1920s.
-
C.
Beatrice Banyard
Beatrice Banyard was an actress and the wife of Canadian-American playwright, actor, and director Willard Mack.
-
D.
Beatrice Dawson
Beatrice Dawson was a British costume designer known for her work on mid-20th-century films, earning multiple Academy Award nominations for her period and character costumes.
-
E.
Beatrice Taylor
Beatrice Taylor is the beloved, nurturing aunt character from "The Andy Griffith Show," best known for her role as Aunt Bee in the fictional town of Mayberry.
- 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: Beatrice Brown Triple: [Mouth to Mouth, starring, Beatrice Brown]
Generated description
Beatrice Brown is an actress known for her role in the film "Mouth to Mouth."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beatrice Brown Target entity description: Beatrice Brown is an actress known for her role in the film "Mouth to Mouth."
-
A.
Beatrice Pearson
Beatrice Pearson was an American film actress best known for her leading role in the 1948 film noir "Force of Evil."
-
B.
Beatrice Weeks
Beatrice Weeks was the second wife of iconic horror film actor Bela Lugosi, briefly married to him in the early 1920s.
-
C.
Beatrice Banyard
Beatrice Banyard was an actress and the wife of Canadian-American playwright, actor, and director Willard Mack.
-
D.
Beatrice Dawson
Beatrice Dawson was a British costume designer known for her work on mid-20th-century films, earning multiple Academy Award nominations for her period and character costumes.
-
E.
Beatrice Taylor
Beatrice Taylor is the beloved, nurturing aunt character from "The Andy Griffith Show," best known for her role as Aunt Bee in the fictional town of Mayberry.
- 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_69e245fb8de081908f0eba7b5fd75bc4 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18efaa1fc81908fb1987dbf732f46 |
completed | April 29, 2026, 4:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c309a54b881909b53677488534b7c |
completed | May 19, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_6a0c3105ab388190b135f5c7de240023 |
completed | May 19, 2026, 9:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c317738d08190adfe51d2b8075f1b |
completed | May 19, 2026, 9:46 a.m. |
Created at: April 17, 2026, 4:01 p.m.