Triple
T2257258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Selasca |
E49755
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Verbania |
E178417
|
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: Verbania | Statement: [Selasca, partOf, Verbania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verbania Context triple: [Selasca, partOf, Verbania]
-
A.
Verbania
chosen
Verbania is a lakeside city in northern Italy, situated on the shores of Lake Maggiore near the Swiss border.
-
B.
Vikrampura
Vikrampura was an important historical city that served as a principal royal center of the medieval Indian Pala dynasty in eastern India.
-
C.
Khandala
Khandala is a popular hill station in Maharashtra, India, known for its scenic valleys, waterfalls, and trekking spots in the Western Ghats.
-
D.
Yavatmal
Yavatmal is a city in the Indian state of Maharashtra, known as a regional center in the Vidarbha area with a predominantly agrarian economy.
-
E.
Benipatti
Benipatti is a town in the Madhubani district of the Indian state of Bihar, known for its rural setting and proximity to the region’s famed Mithila culture.
- 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_69a88aaa9250819095e127d0d77e8a32 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc1570dc88190bb2b17ed4c25dbb5 |
completed | March 7, 2026, 6:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae7f067f208190a399e2b1a83badd1 |
completed | March 9, 2026, 8:04 a.m. |
Created at: March 4, 2026, 7:47 p.m.