Triple
T6032220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hochsauerlandkreis |
E134331
|
entity |
| Predicate | vehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
HSK
HSK is the vehicle registration code for the Hochsauerlandkreis district in the German state of North Rhine-Westphalia.
|
E564069
|
NE FINISHED |
How this triple was built (4 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: [Hochsauerlandkreis, vehicleRegistrationCode, HSK]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HSK Context triple: [Hochsauerlandkreis, vehicleRegistrationCode, HSK]
-
A.
UHSK
UHSK is the ICAO airport code assigned to Severo-Kurilsk Airport in Russia’s Kuril Islands.
-
B.
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.
-
C.
Hanyu Pinyin
Hanyu Pinyin is the official romanization system for Standard Mandarin Chinese, using the Latin alphabet to represent Chinese pronunciation.
-
D.
Mandarin Chinese
Mandarin Chinese is the most widely spoken variety of Chinese and a major world language used across mainland China, Taiwan, and many overseas Chinese communities.
-
E.
Hanja
Hanja is the set of traditional Chinese characters historically used to write Korean, especially for proper names, academic terms, and classical texts.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: HSK Triple: [Hochsauerlandkreis, vehicleRegistrationCode, HSK]
Generated description
HSK is the vehicle registration code for the Hochsauerlandkreis district in the German state of North Rhine-Westphalia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HSK Target entity description: HSK is the vehicle registration code for the Hochsauerlandkreis district in the German state of North Rhine-Westphalia.
-
A.
UHSK
UHSK is the ICAO airport code assigned to Severo-Kurilsk Airport in Russia’s Kuril Islands.
-
B.
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.
-
C.
Hanyu Pinyin
Hanyu Pinyin is the official romanization system for Standard Mandarin Chinese, using the Latin alphabet to represent Chinese pronunciation.
-
D.
Mandarin Chinese
Mandarin Chinese is the most widely spoken variety of Chinese and a major world language used across mainland China, Taiwan, and many overseas Chinese communities.
-
E.
Hanja
Hanja is the set of traditional Chinese characters historically used to write Korean, especially for proper names, academic terms, and classical texts.
- F. None of above. chosen
Provenance (5 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_69c0087515148190a97475d412563865 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056b0a8d081909035e2e85e851ca1 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c113855ad08190b9ff826a2f39c356 |
completed | March 23, 2026, 10:18 a.m. |
| NEDg | Description generation | batch_69c114ec9d0c819092de76a6712c482d |
completed | March 23, 2026, 10:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c115552c188190b500d96e86410180 |
completed | March 23, 2026, 10:26 a.m. |
Created at: March 22, 2026, 4:08 p.m.