Triple
T32570271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria Saal |
E832494
|
entity |
| Predicate | distanceFromKlagenfurt |
P206315
|
FINISHED |
| Object | approximately 10 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 km | Statement: [Maria Saal, distanceFromKlagenfurt, approximately 10 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromKlagenfurt Context triple: [Maria Saal, distanceFromKlagenfurt, approximately 10 km]
-
A.
distanceFromSalzburg
Indicates the spatial distance between a given entity and the city of Salzburg.
-
B.
distanceFromFeldkirch
Indicates the spatial distance measured from the reference location Feldkirch to another place or entity.
-
C.
distanceToInnsbruck
Indicates the spatial distance between a given entity’s location and the city of Innsbruck.
-
D.
distanceToKremsAnDerDonau_km
Indicates the physical distance, measured in kilometers, between an entity and the location Krems an der Donau.
-
E.
distanceFromVienna
Indicates the spatial distance between a given entity’s location and the city of Vienna.
- 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_69f34927bb308190ad94da1b11cad13c |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c7fb9f88190b384b1b68200aef0 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 1, 2026, 1:03 a.m.