Triple
T6819431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chelsea Peretti |
E156859
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Peretti
Peretti is an Italian-origin surname borne by various notable individuals in fields such as entertainment, business, and the arts.
|
E622577
|
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: Peretti | Statement: [Chelsea Peretti, familyName, Peretti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peretti Context triple: [Chelsea Peretti, familyName, Peretti]
-
A.
Petrocelli
Petrocelli is an Italian surname most notably associated with former Boston Red Sox All-Star infielder Rico Petrocelli.
-
B.
Oberto
Oberto is a young boy character in Handel’s opera "Alcina," known for his quest to find his missing father on the enchantress’s island.
-
C.
Gino
Gino is a masculine given name of Italian origin commonly used in Italy and among Italian communities worldwide.
-
D.
Bisciotti
Bisciotti is an Italian surname most prominently associated with Steve Bisciotti, the American billionaire businessman and principal owner of the NFL’s Baltimore Ravens.
-
E.
Cipriani
Cipriani is a surname most prominently associated with Juan Luis Cipriani Thorne, a Peruvian cardinal of the Roman Catholic Church and former Archbishop of Lima.
- 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: Peretti Triple: [Chelsea Peretti, familyName, Peretti]
Generated description
Peretti is an Italian-origin surname borne by various notable individuals in fields such as entertainment, business, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peretti Target entity description: Peretti is an Italian-origin surname borne by various notable individuals in fields such as entertainment, business, and the arts.
-
A.
Petrocelli
Petrocelli is an Italian surname most notably associated with former Boston Red Sox All-Star infielder Rico Petrocelli.
-
B.
Oberto
Oberto is a young boy character in Handel’s opera "Alcina," known for his quest to find his missing father on the enchantress’s island.
-
C.
Gino
Gino is a masculine given name of Italian origin commonly used in Italy and among Italian communities worldwide.
-
D.
Bisciotti
Bisciotti is an Italian surname most prominently associated with Steve Bisciotti, the American billionaire businessman and principal owner of the NFL’s Baltimore Ravens.
-
E.
Cipriani
Cipriani is a surname most prominently associated with Juan Luis Cipriani Thorne, a Peruvian cardinal of the Roman Catholic Church and former Archbishop of Lima.
- 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_69c688298a288190af3f285d57f76bbe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d35781e88190a45d1386706d4422 |
completed | March 27, 2026, 6:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c723e797908190bb0a2d22556b5906 |
completed | March 28, 2026, 12:42 a.m. |
| NEDg | Description generation | batch_69c724e915dc8190a82b69939f78420d |
completed | March 28, 2026, 12:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c728ddadd881909c2faa435031a635 |
completed | March 28, 2026, 1:03 a.m. |
Created at: March 27, 2026, 2:17 p.m.