Triple
T10083491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rixensart |
E213960
|
entity |
| Predicate | hasNeighbouringMunicipality |
P224
|
FINISHED |
| Object | Lasne |
E189049
|
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: Lasne | Statement: [Rixensart, hasNeighbouringMunicipality, Lasne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lasne Context triple: [Rixensart, hasNeighbouringMunicipality, Lasne]
-
A.
Lasne
chosen
Lasne is a picturesque, affluent municipality in Walloon Brabant, Belgium, known for its rural character and high quality of life.
-
B.
Olše
Olše is a river in the eastern Czech Republic that flows through the Moravian-Silesian Region before joining the Oder.
-
C.
Liozna
Liozna is a small settlement in present-day Belarus historically known as the birthplace of the artist Marc Chagall.
-
D.
Lesja
Lesja is a rural municipality in Innlandet county, Norway, known for its mountainous landscapes, agriculture, and outdoor recreation opportunities.
-
E.
Malinska
Malinska is a coastal resort town and popular tourist destination on the island of Krk in Croatia, known for its beaches and Mediterranean atmosphere.
- 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_69ca839bf730819086900c323c9b8c95 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd04352d081908f676444cd2d2578 |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b675f4b08190bd8285f210191b93 |
completed | April 5, 2026, 7:22 p.m. |
Created at: March 30, 2026, 9 p.m.