Triple
T14113683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leiden University Executive Board |
E339708
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Netherlands |
E864
|
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: Netherlands | Statement: [Leiden University Executive Board, location, Netherlands]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Netherlands Context triple: [Leiden University Executive Board, location, Netherlands]
-
A.
Netherlands
chosen
The Netherlands is a Western European country known for its low-lying geography, extensive canal systems, and historically significant role in global trade and European politics.
-
B.
Nederland
Nederland is a small mountain town in Colorado known for its scenic setting near the Rocky Mountains and its quirky local culture.
-
C.
Holland
Holland is a historic coastal region in the western Netherlands that became the political and economic heartland of the emerging Dutch state.
-
D.
Holland
Holland is a common English surname of Dutch origin, historically referring to people from the Holland region of the Netherlands.
-
E.
Holland
Holland is a regional less-than-truckload (LTL) freight carrier in the United States known for its operations in the Midwest and surrounding areas.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de600f992c81908133813f2894dcca |
completed | April 14, 2026, 3:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd0934b74819094ec7309c23a3e2a |
completed | May 7, 2026, 5:49 p.m. |
Created at: April 9, 2026, 10:22 p.m.