Triple

T10072107
Position Surface form Disambiguated ID Type / Status
Subject Irvine E213653 entity
Predicate hasPostcodeArea P920 FINISHED
Object KA
KA is a postcode area in the United Kingdom covering parts of southwest Scotland, including towns such as Kilmarnock and Irvine.
 E839387 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: KA | Statement: [Irvine, hasPostcodeArea, KA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KA
Context triple: [Irvine, hasPostcodeArea, KA]
  • A. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • B. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • C. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • D. KE
    KE is the standard abbreviation for "Kommounistiki Epitheorisi," the theoretical and political journal associated with the Communist Party of Greece.
  • E. KE
    KE is the two-letter ISO 3166-1 alpha-2 country code assigned to Kenya for international identification and data standards.
  • 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: KA
Triple: [Irvine, hasPostcodeArea, KA]
Generated description
KA is a postcode area in the United Kingdom covering parts of southwest Scotland, including towns such as Kilmarnock and Irvine.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KA
Target entity description: KA is a postcode area in the United Kingdom covering parts of southwest Scotland, including towns such as Kilmarnock and Irvine.
  • A. KA
    KA is the vehicle registration code used on license plates for cars registered in the German city of Karlsruhe.
  • B. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • C. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • D. KE
    KE is the standard abbreviation for "Kommounistiki Epitheorisi," the theoretical and political journal associated with the Communist Party of Greece.
  • E. KE
    KE is the IATA airline designator for Korean Air, the flag carrier and largest airline of South Korea.
  • 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_69ca839add308190b57d53b4ec21f2d0 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd013c9d0819091ebe6fc399832de completed April 2, 2026, 2:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29aaa61308190b134b49a6c1c1131 completed April 5, 2026, 5:23 p.m.
NEDg Description generation batch_69d29e8617c08190bf4fb02ac40caba3 completed April 5, 2026, 5:40 p.m.
NED2 Entity disambiguation (via description) batch_69d29f20b2f48190906d7e53fb1e5544 completed April 5, 2026, 5:42 p.m.
Created at: March 30, 2026, 8:59 p.m.