Triple
T1046334
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M7 motorway |
E22586
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Lake Balaton |
E31666
|
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: Lake Balaton | Statement: [M7 motorway, passesNear, Lake Balaton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake Balaton Context triple: [M7 motorway, passesNear, Lake Balaton]
-
A.
Lake Balaton
chosen
Lake Balaton is a major Central European freshwater lake in western Hungary, renowned as a popular tourist and recreation destination.
-
B.
Lake Neusiedl
Lake Neusiedl is a large, shallow steppe lake in Central Europe renowned for its unique wetland ecosystem, birdlife, and surrounding wine-growing region.
-
C.
Großer Wannsee lake
Großer Wannsee lake is a popular recreational lake in southwestern Berlin, known for its beaches, sailing, and proximity to historically significant sites.
-
D.
Lake Velence
Lake Velence is one of Hungary’s largest natural lakes, known as a popular resort and recreation area in the Transdanubian region.
-
E.
Volkerak lake
Volkerak lake is a Dutch freshwater lake in the Rhine–Meuse–Scheldt delta, created as part of the Delta Works water management system.
- 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_69a493d91478819094cc01fb65564bc1 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b84bb0048190badf6d2f7f684d99 |
completed | March 1, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac66224db481909318add535721977 |
completed | March 7, 2026, 5:53 p.m. |
Created at: March 1, 2026, 7:42 p.m.