Triple
T3559604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lambro |
E75302
|
entity |
| Predicate | hasFloodRisk |
P12640
|
FINISHED |
| Object | Monza |
E107899
|
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: Monza | Statement: [Lambro, hasFloodRisk, Monza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monza Context triple: [Lambro, hasFloodRisk, Monza]
-
A.
Monza
chosen
Monza is a historic city in northern Italy renowned for its royal villa and the Autodromo Nazionale Monza Formula One racing circuit.
-
B.
Imola
Imola is a historic city in Italy’s Emilia-Romagna region, best known for its Formula One racing circuit, the Autodromo Enzo e Dino Ferrari.
-
C.
Secchia
The Secchia is a river in northern Italy that flows through the Emilia-Romagna region and is one of the main tributaries contributing to the Po River system.
-
D.
Torino Porta Susa
Torino Porta Susa is a major high-speed and regional railway hub in Turin, Italy, serving as one of the city’s principal train stations.
-
E.
Legnano
Legnano is a town in the Lombardy region of northern Italy, historically known for its medieval Battle of Legnano and its industrial development.
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc089270c81908bc200c84fe1592e |
completed | March 8, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b432fdda488190a31f74e80685c121 |
completed | March 13, 2026, 3:53 p.m. |
Created at: March 8, 2026, 3:20 p.m.