Triple

T17241570
Position Surface form Disambiguated ID Type / Status
Subject Agam Regency E418509 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object BA
BA is the vehicle registration code assigned to motor vehicles registered in Agam Regency, Indonesia.
E1258533 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: BA | Statement: [Agam Regency, hasVehicleRegistrationCode, BA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BA
Context triple: [Agam Regency, hasVehicleRegistrationCode, BA]
  • A. BA
    BA is the New York Stock Exchange ticker symbol for The Boeing Company, a major American aerospace and defense manufacturer.
  • B. BA
    BA is a common abbreviation for Broken Arrow, a suburban city in northeastern Oklahoma.
  • C. BA
    BA is the station code for Bathurst station on the Toronto Transit Commission subway system.
  • D. BA
    BA is the former stock ticker symbol for Bell Aliant, a Canadian telecommunications company that provided internet, phone, and TV services primarily in Atlantic Canada before being acquired by Bell Canada.
  • E. BA
    BA is the station code for Balderas, a metro station in Mexico City’s rapid transit system.
  • 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: BA
Triple: [Agam Regency, hasVehicleRegistrationCode, BA]
Generated description
BA is the vehicle registration code assigned to motor vehicles registered in Agam Regency, Indonesia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: BA
Target entity description: BA is the vehicle registration code assigned to motor vehicles registered in Agam Regency, Indonesia.
  • A. BA
    BA is the regional vehicle registration code assigned to motor vehicles registered in Pesisir Selatan Regency, Indonesia.
  • B. BA
    BA is the vehicle registration code used on license plates for the city and district of Bamberg in Upper Franconia, Germany.
  • C. BA
    BA is the vehicle registration code used on license plates for cars registered in Bratislava, the capital city of Slovakia.
  • D. BA
    BA is a postcode area in southwest England that covers parts of Somerset, including towns such as Bath and Bruton.
  • E. BA
    BA is the station code for Bathurst station on the Toronto Transit Commission subway system.
  • 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_69d886d8e96081909870bff6c3d0bf09 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42e203ec88190a21f38cbb18a14fa completed April 19, 2026, 1:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0170f388608190b709b1c228a7ba29 completed May 11, 2026, 6:02 a.m.
NEDg Description generation batch_6a01718311a48190890c770f571852c8 completed May 11, 2026, 6:04 a.m.
NED2 Entity disambiguation (via description) batch_6a01721f5b9081909a8bc817ba0a5986 completed May 11, 2026, 6:07 a.m.
Created at: April 10, 2026, 5:39 a.m.