Triple
T1563819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Col de Tende |
E33386
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | Cuneo |
E235587
|
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: Cuneo | Statement: [Col de Tende, connects, Cuneo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuneo Context triple: [Col de Tende, connects, Cuneo]
-
A.
Cuneo
chosen
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.
Biella
Biella is a city in the Piedmont region of northern Italy, known for its textile industry and Alpine foothill setting.
-
C.
Alessandria
Alessandria is a city in the Piedmont region of northwestern Italy, known as an important industrial and transportation hub.
-
D.
Parma
Parma is a historic city in northern Italy renowned for its rich artistic heritage, architecture, and culinary traditions, including Parmigiano Reggiano cheese and Parma ham.
-
E.
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.
- 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_69a885ef9cf48190b0af0f5ce3d02231 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9089c7b9881909e44fee8053ac189 |
completed | March 5, 2026, 4:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af5cac2ab08190a41b6ac9802526ee |
completed | March 9, 2026, 11:50 p.m. |
Created at: March 4, 2026, 7:27 p.m.