Triple
T18046163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rheingau-Taunus-Kreis |
E431777
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | RÜD |
E759638
|
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: RÜD | Statement: [Rheingau-Taunus-Kreis, vehicleRegistrationCode, RÜD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RÜD Context triple: [Rheingau-Taunus-Kreis, vehicleRegistrationCode, RÜD]
-
A.
RÜD
chosen
RÜD is the German vehicle registration code for the Rheingau-Taunus-Kreis district in the state of Hesse.
-
B.
RÜG
RÜG is the vehicle registration code used for motor vehicles registered in the district of Vorpommern-Rügen in the German state of Mecklenburg-Vorpommern.
-
C.
RUD
RUD is the Amtrak station code for the Rutland train station in Rutland, Vermont, used for ticketing and scheduling on the U.S. passenger rail network.
-
D.
Rudamun
Rudamun was a little-known pharaoh of ancient Egypt’s Twenty-third Dynasty who ruled in Upper Egypt during the politically fragmented Third Intermediate Period.
-
E.
Ruo
Ruo is one of the small islands that make up the Hall Islands group in the Federated States of Micronesia.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4bff202088190ae971879348e2294 |
completed | April 19, 2026, 11:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0349bb99508190ba6b1f8e4ac2b599 |
completed | May 12, 2026, 3:39 p.m. |
Created at: April 10, 2026, 10:25 a.m.