Triple
T4945643
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baarn railway station |
E111041
|
entity |
| Predicate | locatedInMunicipality |
P40
|
FINISHED |
| Object | Baarn |
E111040
|
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: Baarn | Statement: [Baarn railway station, locatedInMunicipality, Baarn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baarn Context triple: [Baarn railway station, locatedInMunicipality, Baarn]
-
A.
Baarn
chosen
Baarn is a town and municipality in the Dutch province of Utrecht, known for its historic royal connections and green, affluent residential character.
-
B.
Soest
Soest is a historic town in North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and former significance as a Hanseatic trading center.
-
C.
Soest
Soest is a Dutch town and municipality in the central Netherlands known for its green surroundings and proximity to the Utrechtse Heuvelrug.
-
D.
Havelberg
Havelberg is a small historic town in Saxony-Anhalt, Germany, known for its medieval cathedral and location at the confluence of the Havel and Elbe rivers.
-
E.
Werl
Werl is a town in North Rhine-Westphalia, Germany, known for its historical significance and regional correctional facility.
- 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_69bd441721cc819085c7e33fe0876818 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70aa890c81908e685ec5e88cae1f |
completed | March 20, 2026, 4:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81cc862081908b42686f04915238 |
completed | March 21, 2026, 11:32 a.m. |
Created at: March 20, 2026, 1:31 p.m.