Triple
T20156032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lissone |
E491567
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Monza |
—
|
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: Monza | Statement: [Lissone, locatedNear, Monza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monza Context triple: [Lissone, locatedNear, 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.
Mugello
Mugello is a historic rural region in northern Tuscany, Italy, known for its rolling hills, medieval villages, and cultural heritage.
-
C.
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.
-
D.
Royal Park of Monza
The Royal Park of Monza is a vast historic landscaped park in Monza, Italy, renowned as one of Europe’s largest enclosed parks and for hosting the Monza Formula 1 circuit.
-
E.
Monza Circuit
Monza Circuit is a historic Italian motorsport race track, best known as the high-speed home of the Formula One Italian Grand Prix.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667e0a0488190a25d92aaf300be4a |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.