Triple
T21034568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wolfhagen |
E518155
|
entity |
| Predicate | distanceToKassel |
P142557
|
FINISHED |
| Object | approximately 25 km |
—
|
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 25 km | Statement: [Wolfhagen, distanceToKassel, approximately 25 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToKassel Context triple: [Wolfhagen, distanceToKassel, approximately 25 km]
-
A.
distanceToKarlsruhe
Indicates the spatial distance between a given entity and the location of Karlsruhe.
-
B.
relativeLocationToKassel
Indicates a spatial relationship specifying where something is located in relation to Kassel.
-
C.
distanceToKoblenz
Indicates the spatial distance between a given entity and the location of Koblenz.
-
D.
distanceToWiesbaden
Indicates the spatial distance between a given entity or location and the city of Wiesbaden.
-
E.
distanceToDortmund
Indicates the spatial distance between a given entity’s location and the city of Dortmund.
- 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_69e0b503275c8190afd9a163f997c709 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc84b4ac8190bcee5fbba730b499 |
completed | April 21, 2026, 4:26 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2df1a888190b5b478e76bdf7fdf |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:01 p.m.