Triple
T17215621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Enkhuizen |
E417844
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Koepoort |
E508230
|
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: Koepoort | Statement: [Enkhuizen, hasLandmark, Koepoort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koepoort Context triple: [Enkhuizen, hasLandmark, Koepoort]
-
A.
Koepoort
chosen
Koepoort is a historic Dutch city gate known as one of the traditional entrances to a fortified town in the Netherlands.
-
B.
Klaaswaal
Klaaswaal is a village in the Dutch province of South Holland, known for its rural character and location on the island of Hoeksche Waard.
-
C.
Dishoek
Dishoek is a coastal village in the Dutch province of Zeeland, known for its sandy North Sea beaches and seaside tourism.
-
D.
Bergvliet
Bergvliet is a quiet, predominantly residential suburb in Cape Town known for its family-friendly atmosphere, schools, and tree-lined streets.
-
E.
Santpoort
Santpoort is a village in the Dutch province of North Holland, known for its historic estates, dunes, and proximity to the city of Haarlem.
- 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_69d886d779488190b131369541c04e7d |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e42dc9f96881909eb86786a76e17e4 |
completed | April 19, 2026, 1:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a016751a5788190a385774d1ff002d0 |
completed | May 11, 2026, 5:21 a.m. |
Created at: April 10, 2026, 5:38 a.m.