Triple
T9432591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lorenza Izzo |
E227418
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
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."
|
E799895
|
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: Lorenza | Statement: [Lorenza Izzo, givenName, Lorenza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lorenza Context triple: [Lorenza Izzo, givenName, Lorenza]
-
A.
Gianetta
Gianetta is one of the lively, romantic young Venetian girls in Gilbert and Sullivan’s comic opera "The Gondoliers," who becomes entangled in the opera’s mistaken-identity royal plot.
-
B.
Paola
Paola is a feminine given name of Latin origin commonly used in Spanish- and Italian-speaking countries.
-
C.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
-
D.
Paola
Paola is a town in southeastern Malta known for its historic sites, including the prehistoric Ħal Saflieni Hypogeum and other cultural landmarks.
-
E.
Ludovica
Ludovica is an Italian feminine given name, traditionally associated with nobility and derived from the same Germanic roots as names like Louise and Ludwig.
- 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: Lorenza Triple: [Lorenza Izzo, givenName, Lorenza]
Generated description
Lorenza is a Chilean actress and model best known for her roles in films like "Knock Knock" and "Once Upon a Time in Hollywood."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lorenza Target entity description: Lorenza is a Chilean actress and model best known for her roles in films like "Knock Knock" and "Once Upon a Time in Hollywood."
-
A.
Gianetta
Gianetta is one of the lively, romantic young Venetian girls in Gilbert and Sullivan’s comic opera "The Gondoliers," who becomes entangled in the opera’s mistaken-identity royal plot.
-
B.
Paola
Paola is a feminine given name of Latin origin commonly used in Spanish- and Italian-speaking countries.
-
C.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
-
D.
Paola
Paola is a town in southeastern Malta known for its historic sites, including the prehistoric Ħal Saflieni Hypogeum and other cultural landmarks.
-
E.
Ludovica
Ludovica is an Italian feminine given name, traditionally associated with nobility and derived from the same Germanic roots as names like Louise and Ludwig.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e61a114819081fc4a2ad39c96ba |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1104033c08190a3670b017bd984d5 |
completed | April 4, 2026, 1:21 p.m. |
| NEDg | Description generation | batch_69d110ffda7881908e4edd692b818464 |
completed | April 4, 2026, 1:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d11190dd6c8190b6318df44daa9858 |
completed | April 4, 2026, 1:26 p.m. |
Created at: March 30, 2026, 7:49 p.m.