Triple
T23152876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mouth to Mouth |
E578365
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Diana Greenwood
Diana Greenwood is an actress known for her role in the film "Mouth to Mouth."
|
E1579292
|
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: Diana Greenwood | Statement: [Mouth to Mouth, starring, Diana Greenwood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diana Greenwood Context triple: [Mouth to Mouth, starring, Diana Greenwood]
-
A.
Ingrid Pitt
Ingrid Pitt was a Polish-British actress best known as a cult horror icon for her roles in 1970s British genre films.
-
B.
Claire Bloom
Claire Bloom is an acclaimed English actress known for her distinguished stage and screen career, including prominent roles in classic films, television dramas, and Shakespearean productions.
-
C.
Ingrid Caven
Ingrid Caven is a German actress and singer known for her work in European art-house cinema and her collaborations with director Rainer Werner Fassbinder.
-
D.
Liz Robertson
Liz Robertson is a British musical theatre actress and singer known for her performances in West End and Broadway productions.
-
E.
Lorraine Kirke
Lorraine Kirke is a British-born New York boutique owner and costume designer known for her bohemian fashion aesthetic and as the mother of actress Jemima Kirke.
- 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: Diana Greenwood Triple: [Mouth to Mouth, starring, Diana Greenwood]
Generated description
Diana Greenwood 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: Diana Greenwood Target entity description: Diana Greenwood is an actress known for her role in the film "Mouth to Mouth."
-
A.
Ingrid Pitt
Ingrid Pitt was a Polish-British actress best known as a cult horror icon for her roles in 1970s British genre films.
-
B.
Claire Bloom
Claire Bloom is an acclaimed English actress known for her distinguished stage and screen career, including prominent roles in classic films, television dramas, and Shakespearean productions.
-
C.
Ingrid Caven
Ingrid Caven is a German actress and singer known for her work in European art-house cinema and her collaborations with director Rainer Werner Fassbinder.
-
D.
Liz Robertson
Liz Robertson is a British musical theatre actress and singer known for her performances in West End and Broadway productions.
-
E.
Lorraine Kirke
Lorraine Kirke is a British-born New York boutique owner and costume designer known for her bohemian fashion aesthetic and as the mother of actress Jemima Kirke.
- 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_6a0c4c6eb15c8190b5ef79521c5cb4b3 |
completed | May 19, 2026, 11:41 a.m. |
| NEDg | Description generation | batch_6a0c4d521b648190a95a04bd6326ad8d |
completed | May 19, 2026, 11:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c4daeaf5c81909eca82f6d9103b9a |
completed | May 19, 2026, 11:46 a.m. |
Created at: April 17, 2026, 4:01 p.m.