Triple
T17351519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aunt May |
E421823
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object |
Nancy Linari
Nancy Linari is an American actress and voice actress known for her work in animation, television, and video games.
|
E1287540
|
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: Nancy Linari | Statement: [Aunt May, portrayedBy, Nancy Linari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nancy Linari Context triple: [Aunt May, portrayedBy, Nancy Linari]
-
A.
Nancy Barbato
Nancy Barbato was the first wife of singer Frank Sinatra and the mother of their three children, including entertainer Nancy Sinatra.
-
B.
Laura Barbieri
Laura Barbieri is known as the wife of Argentine jazz saxophonist and composer Gato Barbieri.
-
C.
Linda Ercoli
Linda Ercoli is a voice actress known for portraying Peppermint Patty in the Peanuts animated productions.
-
D.
Maria Colacurcio
Maria Colacurcio is an American technology executive and entrepreneur best known as a co-founder of Smartsheet and for her leadership roles in data-driven workplace equity and HR tech companies.
-
E.
Linda Giuva
Linda Giuva is an Italian archivist and academic known for her work in archival science and for being married to former Italian Prime Minister Massimo D'Alema.
- 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: Nancy Linari Triple: [Aunt May, portrayedBy, Nancy Linari]
Generated description
Nancy Linari is an American actress and voice actress known for her work in animation, television, and video games.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nancy Linari Target entity description: Nancy Linari is an American actress and voice actress known for her work in animation, television, and video games.
-
A.
Nancy Barbato
Nancy Barbato was the first wife of singer Frank Sinatra and the mother of their three children, including entertainer Nancy Sinatra.
-
B.
Laura Barbieri
Laura Barbieri is known as the wife of Argentine jazz saxophonist and composer Gato Barbieri.
-
C.
Linda Ercoli
Linda Ercoli is a voice actress known for portraying Peppermint Patty in the Peanuts animated productions.
-
D.
Maria Colacurcio
Maria Colacurcio is an American technology executive and entrepreneur best known as a co-founder of Smartsheet and for her leadership roles in data-driven workplace equity and HR tech companies.
-
E.
Linda Giuva
Linda Giuva is an Italian archivist and academic known for her work in archival science and for being married to former Italian Prime Minister Massimo D'Alema.
- 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_69d889d520008190a26917a95bf1c2ea |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a2ca0708190aae8306ec3a6f2a7 |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02f82126088190ac22448639dafd35 |
completed | May 12, 2026, 9:51 a.m. |
| NEDg | Description generation | batch_6a02f8ba9f3c8190adf8ca9b8a7295a0 |
completed | May 12, 2026, 9:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02f97d8c9081908ecaba1ffdb07332 |
completed | May 12, 2026, 9:57 a.m. |
Created at: April 10, 2026, 5:44 a.m.