Triple
T19015829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kandersteg railway station |
E465345
|
entity |
| Predicate | fareNetwork |
P395
|
FINISHED |
| Object | Libero |
—
|
NE NERFINISHED |
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: Libero | Statement: [Kandersteg railway station, fareNetwork, Libero]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libero Context triple: [Kandersteg railway station, fareNetwork, Libero]
-
A.
Libero
chosen
Libero is a regional public transport fare network and ticketing system serving Bern and surrounding areas in Switzerland.
-
B.
Libera
Libera is a volunteer-run, community-focused IRC network that hosts real-time discussions for free and open-source software projects and related communities.
-
C.
Libera
Libera is an ancient Roman goddess associated with fertility, wine, and freedom, often worshipped alongside Liber and Ceres.
-
D.
Libérat
Libérat is the given name of Libérat Mfumukeko, a Burundian diplomat and former Secretary General of the East African Community.
-
E.
Atleti
Atleti is the commonly used nickname for Atlético de Madrid, a major Spanish professional football club based in Madrid.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd025c188190a1d81f5b4ec7e2c6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6db04fc819094709f223e30a526 |
completed | April 20, 2026, 7:33 a.m. |
Created at: April 10, 2026, 12:02 p.m.