Triple
T15018854
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binnenmaas |
E378028
|
entity |
| Predicate | hasBorderWith |
P224
|
FINISHED |
| Object | Korendijk |
E1192706
|
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: Korendijk | Statement: [Binnenmaas, hasBorderWith, Korendijk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Korendijk Context triple: [Binnenmaas, hasBorderWith, Korendijk]
-
A.
Korendijk
chosen
Korendijk was a former municipality in the Dutch province of South Holland that later became part of the larger municipality of Hoeksche Waard.
-
B.
Nijkerk
Nijkerk is a historic town and municipality in the Dutch province of Gelderland in the central Netherlands.
-
C.
Kortenhoef
Kortenhoef is a village in the Dutch province of North Holland, known for its lakes, peatlands, and scenic natural surroundings.
-
D.
Papendrecht
Papendrecht is a Dutch town situated on the river Merwede in the province of South Holland, known for its residential character and proximity to the city of Dordrecht.
-
E.
Kneuterdijk
Kneuterdijk is a historic street in the center of The Hague, Netherlands, known for its prominent governmental and royal buildings.
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded76445988190984b57de66e00c4a |
completed | April 15, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c28d1e1c81909e869f01659ec233 |
completed | May 10, 2026, 5:38 p.m. |
Created at: April 10, 2026, 2:56 a.m.