Triple

T1486299
Position Surface form Disambiguated ID Type / Status
Subject Worcestershire E29470 entity
Predicate hasPostcodeArea P920 FINISHED
Object WR
WR is the postcode area designation covering Worcester and surrounding parts of Worcestershire in England.
 E169771 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: WR | Statement: [Worcestershire, hasPostcodeArea, WR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WR
Context triple: [Worcestershire, hasPostcodeArea, WR]
  • A. WR
    WR is the abbreviation for the German Council of Science and Humanities, a key advisory body that counsels the German federal and state governments on science, research, and higher education policy.
  • B. W
    The W is a local New York City Subway service that runs on the BMT Broadway Line in Manhattan and Queens, typically operating on weekdays.
  • C. W
    W is one of the iconic white capital letters that make up the famous Hollywood Sign overlooking Los Angeles.
  • D. WN
    WN is the IATA airline designator used to identify Southwest Airlines in flight schedules, ticketing, and aviation operations.
  • E. WW
    WW is the commonly used abbreviation for Woodsworth College, a constituent college of the University of Toronto known for its diverse student body and focus on continuing and part-time education.
  • 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: WR
Triple: [Worcestershire, hasPostcodeArea, WR]
Generated description
WR is the postcode area designation covering Worcester and surrounding parts of Worcestershire in England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WR
Target entity description: WR is the postcode area designation covering Worcester and surrounding parts of Worcestershire in England.
  • A. WR
    WR is the abbreviation for the German Council of Science and Humanities, a key advisory body that counsels the German federal and state governments on science, research, and higher education policy.
  • B. W
    The W is a local New York City Subway service that runs on the BMT Broadway Line in Manhattan and Queens, typically operating on weekdays.
  • C. W
    W is one of the iconic white capital letters that make up the famous Hollywood Sign overlooking Los Angeles.
  • D. WN
    WN is the IATA airline designator used to identify Southwest Airlines in flight schedules, ticketing, and aviation operations.
  • E. WW
    WW is the commonly used abbreviation for Woodsworth College, a constituent college of the University of Toronto known for its diverse student body and focus on continuing and part-time education.
  • 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_69a498da82e08190ba833330d05f380f completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c6a1d8448190b3c90bb82fd806fe completed March 1, 2026, 11:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad15b5aa348190bf6d7a3177eacaff completed March 8, 2026, 6:22 a.m.
NEDg Description generation batch_69ad192ac37c819081aa4bb32e3564d8 completed March 8, 2026, 6:37 a.m.
NED2 Entity disambiguation (via description) batch_69ad19ab136081909c52731e346f2efb completed March 8, 2026, 6:39 a.m.
Created at: March 1, 2026, 8:12 p.m.