Triple
T11369247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saarbrücken |
E269297
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | SB |
E398180
|
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: SB | Statement: [Saarbrücken, vehicleRegistrationCode, SB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SB Context triple: [Saarbrücken, vehicleRegistrationCode, SB]
-
A.
SB
chosen
SB is the vehicle registration code used on license plates for cars registered in the city of Bielsko-Biała in southern Poland.
-
B.
SB
SB was the communist-era secret police and intelligence service of the Polish People's Republic, known for surveillance, repression, and political control.
-
C.
SB
SB is the abbreviated name for the School of Basic Sciences, an academic unit focused on foundational scientific disciplines.
-
D.
SBRY
SBRY is the London Stock Exchange ticker symbol for J Sainsbury plc, one of the United Kingdom’s largest supermarket and retail chains.
-
E.
SJ
SJ is Sweden’s primary state-owned passenger train operator, running long-distance and regional rail services across the country.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea89e1148190b0ca29db9d7e2cbd |
completed | April 9, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5567b67f88190b0412e30b346d36d |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:33 p.m.