Triple
T11103711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Rimini |
E262573
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Talamello
Talamello is a small Italian town in the Emilia-Romagna region, known for its historic hilltop setting and production of traditional fossa cheese.
|
E905461
|
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: Talamello | Statement: [Province of Rimini, contains, Talamello]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Talamello Context triple: [Province of Rimini, contains, Talamello]
-
A.
Schignano
Schignano is a small Italian village in the Lombardy region, known for its traditional Alpine setting and historic Carnival celebrations.
-
B.
Pizzighettone
Pizzighettone is a historic fortified town in northern Italy known for its well-preserved medieval walls and strategic position along the Adda River.
-
C.
Norcino
Norcino is the Italian demonym for a person from Norcia, a town in the Umbria region of central Italy.
-
D.
Verbanesi
Verbanesi are the inhabitants or natives of Verbania, a city in the Piedmont region of northern Italy on the shores of Lake Maggiore.
-
E.
Pianella
Pianella is a small Italian town and comune in the Abruzzo region, known for its historic hilltop center and surrounding olive groves.
- 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: Talamello Triple: [Province of Rimini, contains, Talamello]
Generated description
Talamello is a small Italian town in the Emilia-Romagna region, known for its historic hilltop setting and production of traditional fossa cheese.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Talamello Target entity description: Talamello is a small Italian town in the Emilia-Romagna region, known for its historic hilltop setting and production of traditional fossa cheese.
-
A.
Schignano
Schignano is a small Italian village in the Lombardy region, known for its traditional Alpine setting and historic Carnival celebrations.
-
B.
Pizzighettone
Pizzighettone is a historic fortified town in northern Italy known for its well-preserved medieval walls and strategic position along the Adda River.
-
C.
Norcino
Norcino is the Italian demonym for a person from Norcia, a town in the Umbria region of central Italy.
-
D.
Verbanesi
Verbanesi are the inhabitants or natives of Verbania, a city in the Piedmont region of northern Italy on the shores of Lake Maggiore.
-
E.
Pianella
Pianella is a small Italian town and comune in the Abruzzo region, known for its historic hilltop center and surrounding olive groves.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a2d33948190ac29d174694a78e5 |
completed | April 9, 2026, 12:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d6d03788190acc9748a3ba0d0ab |
completed | April 19, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69e42f3dd02c8190b40bc692c24b2ff4 |
completed | April 19, 2026, 1:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4375eaf448190a17f8df1e83145e0 |
completed | April 19, 2026, 2:01 a.m. |
Created at: April 8, 2026, 9:27 p.m.