Triple
T23984785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vreden |
E604600
|
entity |
| Predicate | distanceToMünster |
P154138
|
FINISHED |
| Object | about 60 km northwest of Münster |
—
|
LITERAL 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: about 60 km northwest of Münster | Statement: [Vreden, distanceToMünster, about 60 km northwest of Münster]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMünster Context triple: [Vreden, distanceToMünster, about 60 km northwest of Münster]
-
A.
distanceToDortmund
Indicates the spatial distance between a given entity’s location and the city of Dortmund.
-
B.
distanceToWuppertal
Indicates the spatial distance between a given entity and the location of Wuppertal.
-
C.
distanceToHöxter
Indicates the spatial distance between a given entity or location and the town of Höxter.
-
D.
distanceToCologne
Indicates the spatial distance between a given entity’s location and the city of Cologne.
-
E.
distanceToOsnabrück
Indicates the spatial distance between a given entity’s location and the city of Osnabrück.
- F. None of above. chosen
Provenance (4 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_69e29543f40c819087700b7a272afb60 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d2c10c708190922daf3b3b9555f4 |
completed | April 29, 2026, 9:43 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:32 p.m.