Triple
T9501149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Province of Milan |
E229141
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Segrate |
E278710
|
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: Segrate | Statement: [Province of Milan, contains, Segrate]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Segrate Context triple: [Province of Milan, contains, Segrate]
-
A.
Segrate
chosen
Segrate is a municipality in the Metropolitan City of Milan in northern Italy, known for its proximity to Milan and its role as a residential and commercial suburb.
-
B.
Rosciano
Rosciano is a small Italian municipality in the Abruzzo region, known for its rural landscape and traditional local agriculture.
-
C.
Merate
Merate is a town in the Lombardy region of northern Italy, known for its historic center and the Merate Astronomical Observatory.
-
D.
Valmadrera
Valmadrera is a town and comune in the Lombardy region of northern Italy, situated near Lake Como and known for its scenic mountainous surroundings.
-
E.
Loiano
Loiano is a small Italian town in the Emilia-Romagna region, known for its Apennine hillside setting and astronomical observatory.
- 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd983d4b708190a4dfef1246986a26 |
completed | April 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b58f7ea08190a96d88bafe9d4308 |
completed | April 5, 2026, 7:18 p.m. |
Created at: March 30, 2026, 7:57 p.m.