Triple
T3604811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnhem Centraal railway station |
E76343
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Winterswijk |
E332470
|
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: Winterswijk | Statement: [Arnhem Centraal railway station, connectsTo, Winterswijk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Winterswijk Context triple: [Arnhem Centraal railway station, connectsTo, Winterswijk]
-
A.
Winterswijk
chosen
Winterswijk is a town in the eastern Netherlands known for its rural landscape, textile-industry history, and location near the German border.
-
B.
Waalwijk
Waalwijk is a town and municipality in the southern Netherlands known historically for its leather and shoe industry.
-
C.
Steenwijk
Steenwijk is a historic town in the Dutch province of Overijssel, known for its medieval center and role as a regional hub in the north of the province.
-
D.
Harderwijk
Harderwijk is a historic Dutch city known for its former Hanseatic trading role and scenic location on the shores of the Veluwemeer.
-
E.
Nieuwendijk
Nieuwendijk is one of Amsterdam’s oldest and busiest shopping streets, running through the historic city center near Dam Square.
- 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_69ad85d93dcc819094fba90cf70f4996 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc1e07bc481908d9fce18d36d8e0d |
completed | March 8, 2026, 6:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef26fd12c819082f644bc727584ec |
completed | April 2, 2026, 10:49 p.m. |
Created at: March 8, 2026, 3:22 p.m.