Triple
T1040186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Piedmontese |
E22452
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object |
Biella
Biella is a city in the Piedmont region of northern Italy, known for its textile industry and Alpine foothill setting.
|
E241495
|
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: Biella | Statement: [Piedmontese, spokenIn, Biella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biella Context triple: [Piedmontese, spokenIn, Biella]
-
A.
Cuneo
Cuneo is a city in the Piedmont region of northwestern Italy, known for its Alpine setting, agricultural traditions, and use of the Piedmontese language.
-
B.
Alessandria
Alessandria is a city in the Piedmont region of northwestern Italy, known as an important industrial and transportation hub.
-
C.
Varese
Varese is a city in northern Italy known for its lakeside setting, surrounding Prealps, and role as an important economic and cultural center in the Lombardy region.
-
D.
Brescia
Brescia is a historic industrial and cultural city in northern Italy, known for its Roman and medieval architecture and its role as an economic hub.
-
E.
Bergamo
Bergamo is a historic city in northern Italy known for its medieval walled upper town, rich artistic heritage, and strategic location at the foothills of the Alps.
- 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: Biella Triple: [Piedmontese, spokenIn, Biella]
Generated description
Biella is a city in the Piedmont region of northern Italy, known for its textile industry and Alpine foothill setting.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Biella Target entity description: Biella is a city in the Piedmont region of northern Italy, known for its textile industry and Alpine foothill setting.
-
A.
Cuneo
Cuneo is a city in the Piedmont region of northwestern Italy, known for its Alpine setting, agricultural traditions, and use of the Piedmontese language.
-
B.
Alessandria
Alessandria is a city in the Piedmont region of northwestern Italy, known as an important industrial and transportation hub.
-
C.
Varese
Varese is a city in northern Italy known for its lakeside setting, surrounding Prealps, and role as an important economic and cultural center in the Lombardy region.
-
D.
Brescia
Brescia is a historic industrial and cultural city in northern Italy, known for its Roman and medieval architecture and its role as an economic hub.
-
E.
Bergamo
Bergamo is a historic city in northern Italy known for its medieval walled upper town, rich artistic heritage, and strategic location at the foothills of the Alps.
- 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b82e4d2c81909ca1264852baf04d |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5d73c10c8190a059c3ae6b3a6279 |
completed | March 9, 2026, 5:41 a.m. |
| NEDg | Description generation | batch_69ae5e027ea48190a052556d43c39f73 |
completed | March 9, 2026, 5:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5ea4edcc81908829e4bd64ce0aea |
completed | March 9, 2026, 5:46 a.m. |
Created at: March 1, 2026, 7:41 p.m.