Triple
T21205393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bersenbrück |
E522563
|
entity |
| Predicate | distanceToOsnabrück |
P143538
|
FINISHED |
| Object | approximately 30 kilometres |
—
|
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: approximately 30 kilometres | Statement: [Bersenbrück, distanceToOsnabrück, approximately 30 kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToOsnabrück Context triple: [Bersenbrück, distanceToOsnabrück, approximately 30 kilometres]
-
A.
distanceToKoblenz
Indicates the spatial distance between a given entity and the location of Koblenz.
-
B.
distanceToBremen
Indicates the spatial distance between a given entity and the location of Bremen.
-
C.
distanceToNuremberg
Indicates the spatial distance between a given entity and the location of Nuremberg.
-
D.
distanceToHöxter
Indicates the spatial distance between a given entity or location and the town of Höxter.
-
E.
distanceToHamburg
Indicates the spatial distance between a given entity’s location and the city of Hamburg.
- 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_69e0b5112d8881909510b2dcdc93106d |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e734342e9081909e241bed54dbc0b4 |
completed | April 21, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69e5f6094e3c81909ee9699e00d371f7 |
completed | April 20, 2026, 9:46 a.m. |
| PDg | Predicate description generation | batch_69e5fa92a2448190896c022dd27511ad |
completed | April 20, 2026, 10:06 a.m. |
Created at: April 16, 2026, 3:20 p.m.