Triple
T24520093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Böheimkirchen |
E606498
|
entity |
| Predicate | distanceToStPoelten |
P156591
|
FINISHED |
| Object | approximately 10–15 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 10–15 km | Statement: [Böheimkirchen, distanceToStPoelten, approximately 10–15 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToStPoelten Context triple: [Böheimkirchen, distanceToStPoelten, approximately 10–15 km]
-
A.
distanceToEisenstadt_km
Indicates the physical distance, measured in kilometers, between a given place and Eisenstadt.
-
B.
distanceFromVienna
Indicates the spatial distance between a given entity’s location and the city of Vienna.
-
C.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
D.
distanceFromSalzburg
Indicates the spatial distance between a given entity and the city of Salzburg.
-
E.
distanceFromBratislava_km
Indicates the distance, measured in kilometers, between a given entity’s location and the city of Bratislava.
- 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_69e2c4c85778819085f5da9af3569ad5 |
completed | April 17, 2026, 11:39 p.m. |
| NER | Named-entity recognition | batch_69f2be044d4c819094e14eda28d371a7 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6b0ca8081908d931aec560eae56 |
completed | April 30, 2026, 12:47 a.m. |
| PDg | Predicate description generation | batch_69f2b8b8bc5881908df49c0b07110246 |
completed | April 30, 2026, 2:04 a.m. |
Created at: April 18, 2026, 2:24 a.m.