Triple
T16342431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doorn |
E396839
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Utrecht |
—
|
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: Utrecht | Statement: [Doorn, hasNearbyCity, Utrecht]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Utrecht Context triple: [Doorn, hasNearbyCity, Utrecht]
-
A.
Utrecht
chosen
Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural center.
-
B.
Utrecht
Utrecht is a small town in South Africa’s KwaZulu-Natal province, known for its scenic surroundings and historical significance dating back to the 19th century.
-
C.
Nijmegen
Nijmegen is a historic Dutch city near the German border that played a crucial strategic role during World War II, particularly in the Allied advance in 1944.
-
D.
Utrecht Terwijde
Utrecht Terwijde is a suburban railway station serving the Terwijde district in the city of Utrecht in the Netherlands.
-
E.
Tilburg
Tilburg is a city in the southern Netherlands known historically as an industrial and textile center and now as a regional cultural and educational hub.
- 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_69d87f26864c819088365ca381a003c2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2da0b88d081908a99cafa8e0ae5db |
completed | April 18, 2026, 1:10 a.m. |
Created at: April 10, 2026, 5:07 a.m.