Triple
T19689691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monza railway station |
E472801
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Seregno |
—
|
NE NERFINISHED |
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: Seregno | Statement: [Monza railway station, connectsTo, Seregno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seregno Context triple: [Monza railway station, connectsTo, Seregno]
-
A.
Seregno
chosen
Seregno is a town in the Lombardy region of northern Italy, known for its industrial activity and proximity to Milan.
-
B.
Segrate
Segrate is a municipality in the Metropolitan City of Milan in northern Italy, known for its proximity to Milan and its role as a residential and commercial suburb.
-
C.
Rosciano
Rosciano is a small Italian municipality in the Abruzzo region, known for its rural landscape and traditional local agriculture.
-
D.
Zagarolo
Zagarolo is a historic hill town in the Lazio region of central Italy, known for its medieval center and proximity to Rome.
-
E.
Novedrate
Novedrate is a small municipality in the Province of Como in the Lombardy region of northern Italy.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6420f1a0c8190ae59aa0ab3ff2802 |
completed | April 20, 2026, 3:11 p.m. |
Created at: April 10, 2026, 1:45 p.m.