Triple
T17332188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vigarano Mainarda |
E420843
|
entity |
| Predicate | bordersWith |
P224
|
FINISHED |
| Object | Bondeno |
E1029034
|
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: Bondeno | Statement: [Vigarano Mainarda, bordersWith, Bondeno]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bondeno Context triple: [Vigarano Mainarda, bordersWith, Bondeno]
-
A.
Bondeno
chosen
Bondeno is a municipality in northern Italy’s Emilia-Romagna region, known for its agricultural landscape and location within the Province of Ferrara.
-
B.
Bondy
Bondy is a suburban commune in the northeastern outskirts of Paris, France, served by regional rail and other public transport links into the capital.
-
C.
Bondo
Bondo is a town in western Kenya’s Nyanza region, known as an administrative and commercial center near Lake Victoria.
-
D.
Bondo
Bondo is a town located in the northeastern part of the Democratic Republic of the Congo.
-
E.
Bondojito
Bondojito is a station on Mexico City’s Metro system, serving passengers on Line 4 in the northern part of the city.
- 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_69d889d3adc881909319f1edb8d2a956 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d6870c8190989897aa6beba8ff |
completed | April 19, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a018c5025d08190ab2581a3b04ae661 |
completed | May 11, 2026, 7:59 a.m. |
Created at: April 10, 2026, 5:43 a.m.