Triple
T16549189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gornergrat |
E402023
|
entity |
| Predicate | distanceFromZermatt |
P124010
|
FINISHED |
| Object | about 10 kilometres by rail |
—
|
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: about 10 kilometres by rail | Statement: [Gornergrat, distanceFromZermatt, about 10 kilometres by rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromZermatt Context triple: [Gornergrat, distanceFromZermatt, about 10 kilometres by rail]
-
A.
distanceToZurich_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Zurich.
-
B.
distanceToJuraMountains
Indicates the spatial distance between a given entity and the Jura Mountains.
-
C.
distanceToAlps
Indicates the spatial distance between a given location or entity and the Alps.
-
D.
distanceToLausanne
Indicates the measured distance between a given entity’s location and the city of Lausanne.
-
E.
distanceToGeneva
Indicates the spatial distance between a given entity and the location of Geneva.
- 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_69d88384bc30819084229e7dcdc39a41 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e34fc323a88190b5c2a34de0a3c7f0 |
completed | April 18, 2026, 9:32 a.m. |
| PD | Predicate disambiguation | batch_69e2969fab208190ad64164d24748c45 |
completed | April 17, 2026, 8:22 p.m. |
| PDg | Predicate description generation | batch_69e2d7f97e548190a474691a152bd8e8 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:15 a.m.