Triple
T10527890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imagen Award |
E248352
|
entity |
| Predicate | founder |
P104
|
FINISHED |
| Object |
Norma Morandini
Norma Morandini is an Argentine journalist, writer, and politician known for her work in human rights advocacy and public service.
|
E875059
|
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: Norma Morandini | Statement: [Imagen Award, founder, Norma Morandini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norma Morandini Context triple: [Imagen Award, founder, Norma Morandini]
-
A.
Daniela Nardini
Daniela Nardini is a Scottish actress best known for her BAFTA-winning role as Anna Forbes in the BBC drama series "This Life."
-
B.
Manuela Testolini
Manuela Testolini is a Canadian businesswoman and philanthropist, known for founding the nonprofit In a Perfect World and for her previous marriage to musician Prince.
-
C.
Zanetta Boni
Zanetta Boni was the mother of Elena Cornaro Piscopia, the 17th-century Venetian scholar renowned as one of the first women to receive a university degree.
-
D.
Mirta Busnelli
Mirta Busnelli is an Argentine actress known for her extensive work in film, television, and theater.
-
E.
Rita Piacenza
Rita Piacenza was the wife of American painter Thomas Hart Benton and a significant presence in his personal and artistic life.
- 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: Norma Morandini Triple: [Imagen Award, founder, Norma Morandini]
Generated description
Norma Morandini is an Argentine journalist, writer, and politician known for her work in human rights advocacy and public service.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Norma Morandini Target entity description: Norma Morandini is an Argentine journalist, writer, and politician known for her work in human rights advocacy and public service.
-
A.
Daniela Nardini
Daniela Nardini is a Scottish actress best known for her BAFTA-winning role as Anna Forbes in the BBC drama series "This Life."
-
B.
Manuela Testolini
Manuela Testolini is a Canadian businesswoman and philanthropist, known for founding the nonprofit In a Perfect World and for her previous marriage to musician Prince.
-
C.
Zanetta Boni
Zanetta Boni was the mother of Elena Cornaro Piscopia, the 17th-century Venetian scholar renowned as one of the first women to receive a university degree.
-
D.
Mirta Busnelli
Mirta Busnelli is an Argentine actress known for her extensive work in film, television, and theater.
-
E.
Rita Piacenza
Rita Piacenza was the wife of American painter Thomas Hart Benton and a significant presence in his personal and artistic life.
- 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509f6f4a88190ae6e0cc0bcbff0c5 |
completed | April 7, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b3d6f6c81908d8247da9d9caab2 |
completed | April 10, 2026, 9:27 p.m. |
| NEDg | Description generation | batch_69d96d85f9648190a43c8c924f5139e3 |
completed | April 10, 2026, 9:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d96e1a36688190b97ced745bc6a30d |
completed | April 10, 2026, 9:39 p.m. |
Created at: April 6, 2026, 12:30 p.m.