Triple
T910969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lombardy |
E19656
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Monza
Monza is a historic city in northern Italy renowned for its royal villa and the Autodromo Nazionale Monza Formula One racing circuit.
|
E107899
|
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: Monza | Statement: [Lombardy, hasMajorCity, Monza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monza Context triple: [Lombardy, hasMajorCity, Monza]
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
Lucca
Lucca is a historic Tuscan city renowned for its well-preserved Renaissance walls, medieval architecture, and charming old town.
-
E.
Sanremo
Sanremo is a coastal city on Italy’s Ligurian Riviera, known as a historic resort destination and host of the annual Sanremo Music Festival.
- 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: Monza Triple: [Lombardy, hasMajorCity, Monza]
Generated description
Monza is a historic city in northern Italy renowned for its royal villa and the Autodromo Nazionale Monza Formula One racing circuit.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monza Target entity description: Monza is a historic city in northern Italy renowned for its royal villa and the Autodromo Nazionale Monza Formula One racing circuit.
-
A.
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.
-
B.
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.
-
C.
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.
-
D.
Lucca
Lucca is a historic Tuscan city renowned for its well-preserved Renaissance walls, medieval architecture, and charming old town.
-
E.
Sanremo
Sanremo is a coastal city on Italy’s Ligurian Riviera, known as a historic resort destination and host of the annual Sanremo Music Festival.
- 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_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2de5b008190851852331db41324 |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7cf5c4acc8190a0f72ca30f187ed1 |
completed | March 4, 2026, 6:21 a.m. |
| NEDg | Description generation | batch_69a7d01af3d48190a0236affa6751a0a |
completed | March 4, 2026, 6:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a7d079234881908826bf24f3c2900a |
completed | March 4, 2026, 6:26 a.m. |
Created at: March 1, 2026, 7:39 p.m.