Triple
T21333935
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Netherlands |
E525986
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Barneveld |
—
|
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: Barneveld | Statement: [Central Netherlands, containsCity, Barneveld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barneveld Context triple: [Central Netherlands, containsCity, Barneveld]
-
A.
Barneveld
chosen
Barneveld is a municipality and town in the Dutch province of Gelderland, known for its poultry industry and central location in the Netherlands.
-
B.
Loosdrecht
Loosdrecht is a Dutch village in the province of North Holland, best known for its scenic lakes and water sports tourism.
-
C.
Benningen
Benningen is a municipality in the Unterallgäu district of Bavaria in southern Germany.
-
D.
Vinkeveen
Vinkeveen is a Dutch village in the province of Utrecht, best known for its lakes and recreational water activities.
-
E.
Bredevoort
Bredevoort is a small historic town in the Dutch province of Gelderland, known for its well-preserved medieval character and its reputation as a national "book town."
- 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_69e0b51b90788190a4dd823d962626da |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee5ba65c4081908b93d5dc6a937cb6 |
completed | April 26, 2026, 6:38 p.m. |
Created at: April 16, 2026, 4:43 p.m.