Triple
T13543961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | De Bilt |
E323463
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Zeist |
—
|
NE NERFINISHED |
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: Zeist | Statement: [De Bilt, adjacentTo, Zeist]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zeist Context triple: [De Bilt, adjacentTo, Zeist]
-
A.
Zeist
chosen
Zeist is a Dutch town and municipality in the central Netherlands known for its historic estates, green surroundings, and proximity to the city of Utrecht.
-
B.
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.
-
C.
Zoeterwoude
Zoeterwoude is a small Dutch municipality and village known for its rural character and location near Leiden in the province of South Holland.
-
D.
Oisterwijk
Oisterwijk is a town in the Dutch province of North Brabant known for its historic center and surrounding forest and fen landscapes.
-
E.
Harderwijk
Harderwijk is a historic Dutch city known for its former Hanseatic trading role and scenic location on the shores of the Veluwemeer.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafda36248190acabde65a88c5471 |
completed | April 12, 2026, 2:44 p.m. |
Created at: April 9, 2026, 9:45 p.m.