Triple
T17369113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Silvana Mangano |
E422262
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Silvana |
E170254
|
NE FINISHED |
How this triple was built (2 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: Silvana | Statement: [Silvana Mangano, givenName, Silvana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silvana Context triple: [Silvana Mangano, givenName, Silvana]
-
A.
Silvana
chosen
Silvana is a feminine given name used in various cultures, often associated with meanings related to forests or woods.
-
B.
Lorenza
Lorenza is a Chilean actress and model best known for her roles in films like "Knock Knock" and "Once Upon a Time in Hollywood."
-
C.
Paola
Paola is a feminine given name of Latin origin commonly used in Spanish- and Italian-speaking countries.
-
D.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
-
E.
Paola
Paola is a town in southeastern Malta known for its historic sites, including the prehistoric Ħal Saflieni Hypogeum and other cultural landmarks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6790448190a24047523a019f81 |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a019566da6c819083b59e0911d02bd5 |
completed | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 5:44 a.m.