Triple
T1101581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francesco Borromini |
E24391
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Bissone |
E89715
|
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: Bissone | Statement: [Francesco Borromini, placeOfBirth, Bissone]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bissone Context triple: [Francesco Borromini, placeOfBirth, Bissone]
-
A.
Bissone
chosen
Bissone is a small Swiss municipality on the shores of Lake Lugano in the canton of Ticino, known for its picturesque lakeside setting and historic village center.
-
B.
Broye
Broye is a river in western Switzerland that flows through the cantons of Fribourg and Vaud before emptying into Lake Neuchâtel.
-
C.
Morges River
The Morges River is a small river in western Switzerland that drains into Lake Geneva near the town of Morges.
-
D.
Thun
Thun is a historic Swiss town in the canton of Bern, known for its medieval old town, lakeside setting on Lake Thun, and views of the surrounding Alps.
-
E.
Aare basin
The Aare basin is a major river catchment area in Switzerland that collects waters from numerous lakes and tributaries before ultimately feeding into the Rhine.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9c21c2c8190a34d91a7afed23a9 |
completed | March 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8309594c8190986b048b8982f153 |
completed | March 7, 2026, 7:56 p.m. |
Created at: March 1, 2026, 7:43 p.m.