Triple
T36593853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lutry railway station |
E902740
|
entity |
| Predicate | distanceFromLausanne |
P100548
|
FINISHED |
| Object | approximately 5 km east |
—
|
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 5 km east | Statement: [Lutry railway station, distanceFromLausanne, approximately 5 km east]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromLausanne Context triple: [Lutry railway station, distanceFromLausanne, approximately 5 km east]
-
A.
distanceToLausanne
chosen
Indicates the measured distance between a given entity’s location and the city of Lausanne.
-
B.
distanceToGeneva
Indicates the spatial distance between a given entity and the location of Geneva.
-
C.
distanceToZurich_km
Indicates the physical distance, measured in kilometers, between an entity’s location and the city of Zurich.
-
D.
distanceToLugano
Indicates the spatial distance between a given entity and the location of Lugano.
-
E.
distanceToLucerne
Indicates the spatial distance between an entity and the location Lucerne.
- 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_69f76e6592e88190bac4eb00a46e9df9 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0bf4b88190bdcfae9a14b51f0a |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:11 p.m.