Triple
T9498811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weiden in der Oberpfalz |
E229081
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | WEN |
E802085
|
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: WEN | Statement: [Weiden in der Oberpfalz, vehicleRegistrationCode, WEN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WEN Context triple: [Weiden in der Oberpfalz, vehicleRegistrationCode, WEN]
-
A.
WEN
chosen
WEN is the vehicle registration code for the German city of Weiden in der Oberpfalz in Bavaria.
-
B.
Wen
Wen is the given name of Sun I-hsien, a person identifiable by this personal name within Chinese naming conventions.
-
C.
WHE
WHE is the National Rail station code for Whalley railway station in Lancashire, England.
-
D.
WUN
WUN is a global consortium of research-intensive universities that collaborate on international education and research initiatives.
-
E.
WUN
WUN is the vehicle registration code for the district of Wunsiedel im Fichtelgebirge in Upper Franconia, 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_69ca84753660819098e8d416e89e26ae |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd983a94c48190a7ddf95a953c4ecc |
completed | April 1, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d13a0a5ec881908bb1643d2bea2c9f |
completed | April 4, 2026, 4:19 p.m. |
Created at: March 30, 2026, 7:56 p.m.