Triple
T5764131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enzo Ferrari |
E127168
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Maranello, Italy |
E460157
|
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: Maranello, Italy | Statement: [Enzo Ferrari, residence, Maranello, Italy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maranello, Italy Context triple: [Enzo Ferrari, residence, Maranello, Italy]
-
A.
Maranello, Italy
chosen
Maranello, Italy is a town in the Emilia-Romagna region best known as the home of the Ferrari automobile factory and museum.
-
B.
Monza
Monza is a historic city in northern Italy renowned for its royal villa and the Autodromo Nazionale Monza Formula One racing circuit.
-
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.
Montella, Italy
Montella, Italy is a small town in the Campania region of southern Italy, known for its mountainous landscape and traditional chestnut production.
-
E.
Calenzano, Italy
Calenzano, Italy is a Tuscan municipality near Florence known for its medieval castle, historic hilltop village, and mix of industrial and residential areas.
- 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_69c00833a3fc81908f4bc29ed011b7a6 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0296e12d48190bd120879723bb6e8 |
completed | March 22, 2026, 5:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07e59c2d0819091101dea300e1d7e |
completed | March 22, 2026, 11:42 p.m. |
Created at: March 22, 2026, 3:49 p.m.