Triple

T14123935
Position Surface form Disambiguated ID Type / Status
Subject Saarlouis E339974 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object SLS
SLS is the vehicle registration code used on license plates for vehicles registered in Saarlouis, Germany.
 E1082522 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: SLS | Statement: [Saarlouis, vehicleRegistrationCode, SLS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SLS
Context triple: [Saarlouis, vehicleRegistrationCode, SLS]
  • A. SLS
    SLS is NASA’s heavy-lift, super-heavy rocket designed to carry astronauts and large payloads beyond low Earth orbit, including missions to the Moon and eventually Mars.
  • B. SLS
    SLS is a Finnish scholarly society dedicated to the preservation, research, and promotion of Swedish-language literature and cultural heritage in Finland.
  • C. SLM
    SLM is the stock ticker symbol for Sanlam, a major South African financial services group offering insurance, investment, and wealth management products.
  • D. SLSF
    SLSF was the reporting mark used by the St. Louis–San Francisco Railway, commonly known as the Frisco, a major Midwestern and Southern U.S. railroad.
  • E. SLD
    SLD was a particle physics experiment at the SLAC Linear Collider that made precision measurements of electroweak interactions, including properties of the Z boson.
  • 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: SLS
Triple: [Saarlouis, vehicleRegistrationCode, SLS]
Generated description
SLS is the vehicle registration code used on license plates for vehicles registered in Saarlouis, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SLS
Target entity description: SLS is the vehicle registration code used on license plates for vehicles registered in Saarlouis, Germany.
  • A. SLS
    SLS is NASA’s heavy-lift, super-heavy rocket designed to carry astronauts and large payloads beyond low Earth orbit, including missions to the Moon and eventually Mars.
  • B. SLS
    SLS is a Finnish scholarly society dedicated to the preservation, research, and promotion of Swedish-language literature and cultural heritage in Finland.
  • C. SLM
    SLM is the stock ticker symbol for Sanlam, a major South African financial services group offering insurance, investment, and wealth management products.
  • D. SLSF
    SLSF was the reporting mark used by the St. Louis–San Francisco Railway, commonly known as the Frisco, a major Midwestern and Southern U.S. railroad.
  • E. SLD
    SLD was a particle physics experiment at the SLAC Linear Collider that made precision measurements of electroweak interactions, including properties of the Z boson.
  • 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_69d81c6a95b481909e39111e0c1f31ee completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de6095548881908a9e66adccca92d2 completed April 14, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdf0a7a7c8190860d8ce47b5f0732 completed May 7, 2026, 6:50 p.m.
NEDg Description generation batch_69fce0dec2488190be9c24d3744e7243 completed May 7, 2026, 6:58 p.m.
NED2 Entity disambiguation (via description) batch_69fce206b0588190a0f4b24231d3c365 completed May 7, 2026, 7:03 p.m.
Created at: April 9, 2026, 10:22 p.m.