Triple
T15909279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Franzese |
E385803
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Franzese
Franzese is an Italian surname borne by various notable individuals in fields such as entertainment and organized crime.
|
E1183722
|
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: Franzese | Statement: [Daniel Franzese, familyName, Franzese]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Franzese Context triple: [Daniel Franzese, familyName, Franzese]
-
A.
French
French is a Romance language that evolved from Latin and is now spoken worldwide as both a native and official language in many countries.
-
B.
French
French is a common English-language surname of French origin borne by various notable individuals, including philanthropist Melinda Ann French (Melinda Gates).
-
C.
Louis (French)
Louis is the French given name corresponding to the name Ludwik in other languages.
-
D.
The French
The French is a renowned fine-dining restaurant in Manchester’s Midland Hotel, known for its modern British cuisine and historic, elegant setting.
-
E.
FR-EE
FR-EE is an international architecture and design firm known for its innovative, futuristic projects and urban-scale developments led by Mexican architect Fernando Romero.
- 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: Franzese Triple: [Daniel Franzese, familyName, Franzese]
Generated description
Franzese is an Italian surname borne by various notable individuals in fields such as entertainment and organized crime.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Franzese Target entity description: Franzese is an Italian surname borne by various notable individuals in fields such as entertainment and organized crime.
-
A.
French
French is a common English-language surname of French origin borne by various notable individuals, including philanthropist Melinda Ann French (Melinda Gates).
-
B.
French
French is a Romance language that evolved from Latin and is now spoken worldwide as both a native and official language in many countries.
-
C.
Louis (French)
Louis is the French given name corresponding to the name Ludwik in other languages.
-
D.
The French
The French is a renowned fine-dining restaurant in Manchester’s Midland Hotel, known for its modern British cuisine and historic, elegant setting.
-
E.
FR-EE
FR-EE is an international architecture and design firm known for its innovative, futuristic projects and urban-scale developments led by Mexican architect Fernando Romero.
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1565d2f048190a40379ceae00411a |
completed | April 16, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb055307081908a13c98a0e16780c |
completed | May 9, 2026, 10:08 p.m. |
| NEDg | Description generation | batch_69ffb110a5b88190904f763057e8eb1e |
completed | May 9, 2026, 10:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffb1a5e9b88190b790c81b9500c2ac |
completed | May 9, 2026, 10:13 p.m. |
Created at: April 10, 2026, 4:52 a.m.