Triple
T12406362
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Werra-Meißner-Kreis |
E296396
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object | ESW |
E852248
|
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: ESW | Statement: [Werra-Meißner-Kreis, vehicleRegistrationCode, ESW]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ESW Context triple: [Werra-Meißner-Kreis, vehicleRegistrationCode, ESW]
-
A.
ESW
chosen
ESW is the vehicle registration code for the German town and district of Eschwege in the state of Hesse.
-
B.
EWS
EWS (English, Welsh & Scottish Railway) was a major British rail freight company that operated a large fleet of locomotives and became the dominant freight operator in the UK before being rebranded as DB Cargo UK.
-
C.
WES
WES is a commuter rail service in the Portland, Oregon metropolitan area that connects Beaverton and Wilsonville.
-
D.
WES
WES is the vehicle registration code used on license plates for vehicles registered in the Wesel district of Germany.
-
E.
WES
WES is the three-letter station code used to identify Westminster Underground Station on the London Underground network.
- 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_69d6ad9f464c81909db36d7e96e34b9e |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d48f1908190918551c794f98fe3 |
completed | April 10, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f63488cac08190a81b2151c827932e |
completed | May 2, 2026, 5:29 p.m. |
Created at: April 8, 2026, 9:55 p.m.