Triple
T4315622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwalm-Eder region |
E94182
|
entity |
| Predicate | relativeLocationToKassel |
P56415
|
FINISHED |
| Object | south of Kassel |
—
|
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: south of Kassel | Statement: [Schwalm-Eder region, relativeLocationToKassel, south of Kassel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relativeLocationToKassel Context triple: [Schwalm-Eder region, relativeLocationToKassel, south of Kassel]
-
A.
distanceToBasel_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Basel.
-
B.
distanceToStuttgart
Indicates the measured distance between a given entity’s location and the city of Stuttgart.
-
C.
distanceToUlm
Indicates the spatial distance between a given entity or location and the city of Ulm.
-
D.
distanceToLeipzig
Indicates the spatial distance between a given entity’s location and the city of Leipzig.
-
E.
distanceToDresden
Indicates the spatial distance between a given entity or location and the city of Dresden.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350f60dfc819098202b7eb3bbf402 |
completed | March 12, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69b34f4a07b08190a06ada0d9cbb14fb |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b35034cd248190bae09e9d090e13ec |
completed | March 12, 2026, 11:45 p.m. |
Created at: March 12, 2026, 11:12 p.m.