Triple
T907349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brouwersdam |
E19577
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Ouddorp |
E233209
|
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: Ouddorp | Statement: [Brouwersdam, near, Ouddorp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ouddorp Context triple: [Brouwersdam, near, Ouddorp]
-
A.
Ouddorp
chosen
Ouddorp is a coastal village in the Netherlands known for its wide North Sea beaches and dunes, popular for tourism and water sports.
-
B.
Alblasserdam
Alblasserdam is a town and municipality in the western Netherlands, situated along the Noord River and known for its proximity to the Kinderdijk windmills.
-
C.
Brouwershaven
Brouwershaven is a small historic town in the Dutch province of Zeeland, known for its maritime heritage and well-preserved old center.
-
D.
Sneek
Sneek is a historic Dutch city known for its waterways, sailing culture, and the iconic Waterpoort gate.
-
E.
Naaldwijk
Naaldwijk is a town in the Westland municipality of the western Netherlands, known for its extensive greenhouse horticulture and flower industry.
- 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2cdc1788190a704809404f49986 |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5626ad7208190afcc45d36a87d82d |
completed | March 14, 2026, 1:28 p.m. |
Created at: March 1, 2026, 7:39 p.m.