Triple
T17919377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neuenegg |
E448024
|
entity |
| Predicate | distanceToBernApproxKm |
P60858
|
FINISHED |
| Object | 10–15 |
—
|
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: 10–15 | Statement: [Neuenegg, distanceToBernApproxKm, 10–15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToBernApproxKm Context triple: [Neuenegg, distanceToBernApproxKm, 10–15]
-
A.
distanceToBern_km
chosen
Indicates the distance, measured in kilometers, between an entity’s location and the city of Bern.
-
B.
distanceToBasel_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Basel.
-
C.
distanceToZurich_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Zurich.
-
D.
distanceToEisenstadt_km
Indicates the physical distance, measured in kilometers, between a given place and Eisenstadt.
-
E.
distanceToLausanne
Indicates the measured distance between a given entity’s location and the city of Lausanne.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a30844548190b7a43c2f093f35d7 |
completed | April 19, 2026, 9:40 a.m. |
| PD | Predicate disambiguation | batch_69e3d8ec2f6881909d7f54b878cbed37 |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:20 a.m.