Triple
T16783581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Chaux-de-Fonds railway station |
E407912
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object | FFS |
E112375
|
NE 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: FFS | Statement: [La Chaux-de-Fonds railway station, hasAbbreviation, FFS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FFS Context triple: [La Chaux-de-Fonds railway station, hasAbbreviation, FFS]
-
A.
FFS
chosen
FFS is the commonly used abbreviation for the Swiss Federal Railways, the national railway company of Switzerland.
-
B.
FFS
FFS is the state agency responsible for managing and protecting Florida’s forest resources, including wildfire prevention, suppression, and sustainable forestry.
-
C.
FFS
FFS is the abbreviation for the Football Federation Samoa, the governing body responsible for overseeing football activities in Samoa.
-
D.
FFS
FFS is a high-performance file system originally developed for BSD Unix that introduced improved disk layout and efficiency over earlier Unix file systems.
-
E.
FFS
FFS is the station code for Frankfurt (Main) Süd, a major railway station in Frankfurt, Germany.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8839270588190886720d9519bbf8f |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b218d31881908d896e688ebd171c |
completed | April 18, 2026, 4:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00ab056dc88190aeb9e135ae955125 |
completed | May 10, 2026, 3:57 p.m. |
Created at: April 10, 2026, 5:22 a.m.