Triple

T10442123
Position Surface form Disambiguated ID Type / Status
Subject Hof district E246193 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object SAN
SAN is the vehicle registration code used on license plates for vehicles registered in the Hof district of Bavaria, Germany.
 E863175 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: SAN | Statement: [Hof district, vehicleRegistrationCode, SAN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SAN
Context triple: [Hof district, vehicleRegistrationCode, SAN]
  • A. SAN
    SAN is the three-letter IATA airport code for San Diego International Airport, the primary commercial airport serving the San Diego, California area.
  • B. SAN
    SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
  • C. SAN
    SAN is the stock ticker symbol for Banco Santander S.A., a major Spanish multinational banking and financial services company.
  • D. SAM
    SAM is the official FIFA trigramme used to represent the Samoa national under-20 football team in international competitions and records.
  • E. SAM
    SAM is the commonly used abbreviation for the South Australian Museum, a major natural history and cultural institution located in Adelaide, Australia.
  • 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: SAN
Triple: [Hof district, vehicleRegistrationCode, SAN]
Generated description
SAN is the vehicle registration code used on license plates for vehicles registered in the Hof district of Bavaria, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SAN
Target entity description: SAN is the vehicle registration code used on license plates for vehicles registered in the Hof district of Bavaria, Germany.
  • A. SAN
    SAN is the three-letter IATA airport code for San Diego International Airport, the primary commercial airport serving the San Diego, California area.
  • B. SAN
    SAN is the acronym for the Schuylkill Action Network, a collaborative partnership focused on protecting and restoring the Schuylkill River watershed.
  • C. SAN
    SAN is the stock ticker symbol for Banco Santander S.A., a major Spanish multinational banking and financial services company.
  • D. SAM
    SAM is the official FIFA trigramme used to represent the Samoa national under-20 football team in international competitions and records.
  • E. SAM
    SAM is the commonly used abbreviation for the South Australian Museum, a major natural history and cultural institution located in Adelaide, Australia.
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fb9ebf488190ae776bd65e94cb00 completed April 7, 2026, 12:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d87ed6edd88190afd5063daba58a46 completed April 10, 2026, 4:38 a.m.
NEDg Description generation batch_69d8837f98e08190bbffa535f94daf48 completed April 10, 2026, 4:58 a.m.
NED2 Entity disambiguation (via description) batch_69d889d0e70c8190953d164f34f01e47 completed April 10, 2026, 5:25 a.m.
Created at: April 6, 2026, 12:15 p.m.