Triple
T17102588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lünen |
E415014
|
entity |
| Predicate | distanceToDortmund |
P125932
|
FINISHED |
| Object | about 15 km north of Dortmund city centre |
—
|
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 15 km north of Dortmund city centre | Statement: [Lünen, distanceToDortmund, about 15 km north of Dortmund city centre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToDortmund Context triple: [Lünen, distanceToDortmund, about 15 km north of Dortmund city centre]
-
A.
distanceToHamburg
Indicates the spatial distance between a given entity’s location and the city of Hamburg.
-
B.
distanceToFrankfurt
Indicates the spatial distance between a given location or entity and the city of Frankfurt.
-
C.
distanceToBremen
Indicates the spatial distance between a given entity and the location of Bremen.
-
D.
distanceToBerlin
Indicates the spatial distance between a given entity’s location and the city of Berlin.
-
E.
distanceToKoblenz
Indicates the spatial distance between a given entity and the location of Koblenz.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2495c88190b5b16a006a994faf |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d6b1b988190a8d6b6fe78c35e59 |
completed | April 18, 2026, 10:31 a.m. |
| PDg | Predicate description generation | batch_69e37542d060819082aa73948eb8ebd4 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:35 a.m.