Triple
T27323899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaterstetten |
E689585
|
entity |
| Predicate | distanceToMunichCityCentre_km |
P84034
|
FINISHED |
| Object | approximately 15 |
—
|
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 | Statement: [Vaterstetten, distanceToMunichCityCentre_km, approximately 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMunichCityCentre_km Context triple: [Vaterstetten, distanceToMunichCityCentre_km, approximately 15]
-
A.
distanceToMunich
chosen
Indicates the spatial distance between a given entity’s location and the city of Munich.
-
B.
distanceToFrankfurtAirport_km
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
C.
distanceToKoblenz
Indicates the spatial distance between a given entity and the location of Koblenz.
-
D.
distanceFromVienna
Indicates the spatial distance between a given entity’s location and the city of Vienna.
-
E.
directionFromMunich
Indicates the cardinal or relative compass direction of one place or object as measured outward starting from Munich.
- F. None of above.
Provenance (3 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_69ef355d4cb08190ab032c0a2e7d3753 |
completed | April 27, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69ff2fbae9b48190847eefa1c227d43e |
completed | May 9, 2026, 12:59 p.m. |
| PD | Predicate disambiguation | batch_69ff2f2218048190a32224a648182b5d |
completed | May 9, 2026, 12:57 p.m. |
Created at: April 27, 2026, 11:34 a.m.