Triple
T18480994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hallein |
E451555
|
entity |
| Predicate | distanceFromSalzburg |
P131820
|
FINISHED |
| Object | approximately 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 15 km | Statement: [Hallein, distanceFromSalzburg, approximately 15 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromSalzburg Context triple: [Hallein, distanceFromSalzburg, approximately 15 km]
-
A.
distanceToInnsbruck
Indicates the spatial distance between a given entity’s location and the city of Innsbruck.
-
B.
distanceToEisenstadt_km
Indicates the physical distance, measured in kilometers, between a given place and Eisenstadt.
-
C.
distanceFromVienna
Indicates the spatial distance between a given entity’s location and the city of Vienna.
-
D.
distanceToRosenheim
Indicates the spatial distance between a given entity and the location Rosenheim.
-
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_69d8d38465a0819099b9b42d2a662ac1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e531d3d13c81909c52d797360b840a |
completed | April 19, 2026, 7:49 p.m. |
| PD | Predicate disambiguation | batch_69e469d671088190b619de96ea6f92ab |
completed | April 19, 2026, 5:36 a.m. |
| PDg | Predicate description generation | batch_69e46d2aa72c8190a40854a7a52081e2 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 11:35 a.m.