Triple
T13021689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harderwijk |
E326186
|
entity |
| Predicate | locatedOn |
P40
|
FINISHED |
| Object | Veluwemeer |
E600142
|
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: Veluwemeer | Statement: [Harderwijk, locatedOn, Veluwemeer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Veluwemeer Context triple: [Harderwijk, locatedOn, Veluwemeer]
-
A.
Veluwemeer
chosen
Veluwemeer is a shallow border lake in the central Netherlands, popular for water sports, recreation, and nature conservation.
-
B.
Uitgeestermeer
Uitgeestermeer is a lake in North Holland, the Netherlands, known for its recreational boating, sailing, and natural wetland surroundings near the town of Uitgeest.
-
C.
Oostvoornse Meer
Oostvoornse Meer is a recreational lake in the Dutch province of South Holland, popular for activities such as diving, windsurfing, and nature walks.
-
D.
Damsterdiep
Damsterdiep is a historic canal in the Dutch province of Groningen that connects the city of Groningen with the town of Appingedam and the Ems estuary.
-
E.
Veerse Meer
Veerse Meer is a coastal lagoon and recreational lake in the Dutch province of Zeeland, popular for water sports and nature conservation.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ed05e9c8190a4f208662bca0602 |
completed | April 10, 2026, 10:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c119e19c81908ae2b1caff6f2f32 |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:52 p.m.