Triple

T8031796
Position Surface form Disambiguated ID Type / Status
Subject Braunsbedra E187000 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object SK
SK is the vehicle registration code assigned to the German district of Saalekreis in the state of Saxony-Anhalt.
 E709196 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: SK | Statement: [Braunsbedra, vehicleRegistrationCode, SK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SK
Context triple: [Braunsbedra, vehicleRegistrationCode, SK]
  • A. SK
    SK is the postcode area covering Stockport and surrounding parts of Greater Manchester and nearby counties in North West England.
  • B. SK
    SK is the vehicle registration code used on license plates for vehicles registered in Skopje, the capital city of North Macedonia.
  • C. SK
    SK is the IATA airline designator used worldwide to identify Scandinavian Airlines on tickets, timetables, and flight information systems.
  • D. SK
    SK is the ISO 3166-1 alpha-2 country code for Slovakia, a landlocked Central European nation known for its mountains, castles, and membership in the European Union.
  • E. SK
    SK is the official vehicle registration code assigned to the Indian state of Sikkim.
  • 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: SK
Triple: [Braunsbedra, vehicleRegistrationCode, SK]
Generated description
SK is the vehicle registration code assigned to the German district of Saalekreis in the state of Saxony-Anhalt.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SK
Target entity description: SK is the vehicle registration code assigned to the German district of Saalekreis in the state of Saxony-Anhalt.
  • A. SK chosen
    SK is the vehicle registration code for the district of Saalekreis in the German state of Saxony-Anhalt.
  • B. SK
    SK is the official vehicle registration code assigned to the Indian state of Sikkim.
  • C. SK
    SK is the vehicle registration code used on license plates for vehicles registered in Skopje, the capital city of North Macedonia.
  • D. SK
    SK is the ISO 3166-1 alpha-2 country code for Slovakia, a landlocked Central European nation known for its mountains, castles, and membership in the European Union.
  • E. SK
    SK is the postcode area covering Stockport and surrounding parts of Greater Manchester and nearby counties in North West England.
  • F. None of above.

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_69ca82ae2d1081909dbfee42b41db419 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ef18da48190835454a5eb969da7 completed March 31, 2026, 3:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc93bc11108190a34a35d0022f4bfd completed April 1, 2026, 3:40 a.m.
NEDg Description generation batch_69cc9557c6148190a759021b6add0a61 completed April 1, 2026, 3:47 a.m.
NED2 Entity disambiguation (via description) batch_69cc96a8bb688190a352de1798b380f1 completed April 1, 2026, 3:53 a.m.
Created at: March 30, 2026, 5:22 p.m.