Triple

T14951080
Position Surface form Disambiguated ID Type / Status
Subject Weimarer Land E372793 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object SOM
SOM is the vehicle registration code used on license plates for vehicles registered in the Weimarer Land district of Thuringia, Germany.
 E1129111 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: SOM | Statement: [Weimarer Land, hasVehicleRegistrationCode, SOM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SOM
Context triple: [Weimarer Land, hasVehicleRegistrationCode, SOM]
  • A. SOM
    SOM is the three-letter ISO 3166-1 alpha-3 country code assigned to Somalia.
  • B. SOM
    SOM is the vehicle registration code used on license plates for vehicles registered in Somogy County, Hungary.
  • C. SOM
    SOM is an abbreviation used by the U.S. Embassy in Ankara, likely referring to a specific mission, office, or program within the embassy’s organizational structure.
  • D. SOM
    SOM is the commonly used abbreviation for Stade Olympique Montpelliérain, a French sports club based in Montpellier.
  • E. SOM
    SOM is a Canadian provincial honorific suffix indicating membership in the Saskatchewan Order of Merit.
  • 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: SOM
Triple: [Weimarer Land, hasVehicleRegistrationCode, SOM]
Generated description
SOM is the vehicle registration code used on license plates for vehicles registered in the Weimarer Land district of Thuringia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SOM
Target entity description: SOM is the vehicle registration code used on license plates for vehicles registered in the Weimarer Land district of Thuringia, Germany.
  • A. SOM
    SOM is the vehicle registration code used on license plates for vehicles registered in Somogy County, Hungary.
  • B. SOM
    SOM is the three-letter ISO 3166-1 alpha-3 country code assigned to Somalia.
  • C. SOM
    SOM is a Canadian provincial honorific suffix indicating membership in the Saskatchewan Order of Merit.
  • D. SOM
    SOM is the commonly used abbreviation for Stade Olympique Montpelliérain, a French sports club based in Montpellier.
  • E. SOM
    SOM is an abbreviation used by the U.S. Embassy in Ankara, likely referring to a specific mission, office, or program within the embassy’s organizational structure.
  • 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_69d85cca979481908747d2a81eba1cea completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded690f2e08190ad9dad6dc05a164a completed April 15, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e9a6098819087b6e81bebcf6805 completed May 9, 2026, 12:23 a.m.
NEDg Description generation batch_69fe83fdef58819098cda8cca0d810dd completed May 9, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_69fe8518121c8190b6ffcc14ec5ac8ea completed May 9, 2026, 12:51 a.m.
Created at: April 10, 2026, 2:39 a.m.