Triple
T14495603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnsberg |
E359488
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object | HSK |
E564069
|
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: HSK | Statement: [Arnsberg, hasVehicleRegistrationCode, HSK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HSK Context triple: [Arnsberg, hasVehicleRegistrationCode, HSK]
-
A.
HSK
chosen
HSK is the vehicle registration code for the Hochsauerlandkreis district in the German state of North Rhine-Westphalia.
-
B.
UHSK
UHSK is the ICAO airport code assigned to Severo-Kurilsk Airport in Russia’s Kuril Islands.
-
C.
MHK
MHK is the post-nominal abbreviation used by elected members of the House of Keys, the lower branch of the Isle of Man's parliament.
-
D.
Gaokao
Gaokao is China’s highly competitive national college entrance examination that largely determines students’ access to universities and future educational opportunities.
-
E.
Hanyu Pinyin
Hanyu Pinyin is the official romanization system for Standard Mandarin Chinese, using the Latin alphabet to represent Chinese pronunciation.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de93109cb081909a6e846db23a4635 |
completed | April 14, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d9731588190b27a826582e5fc6d |
completed | May 8, 2026, 4:59 a.m. |
Created at: April 10, 2026, 1:21 a.m.