Triple

T19722221
Position Surface form Disambiguated ID Type / Status
Subject Litochoro E473638 entity
Predicate hasLicensePlateCode P68833 FINISHED
Object KN NE NERFINISHED

How this triple was built (2 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: KN | Statement: [Litochoro, hasLicensePlateCode, KN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: KN
Context triple: [Litochoro, hasLicensePlateCode, KN]
  • A. KN
    KN is the IATA airline designator assigned to China United Airlines, a Chinese domestic carrier based in Beijing.
  • B. KN
    KN is the vehicle registration code used for cars registered in the town of Kolárovo in Slovakia.
  • C. KN
    KN is the ISO 3166-1 alpha-2 country code for Saint Kitts and Nevis, a dual-island nation in the Caribbean.
  • D. KN chosen
    KN is the vehicle registration code assigned to the city of Konstanz and its surrounding district in the German state of Baden-Württemberg.
  • E. KEN
    KEN is the official FIFA trigramme used to represent the Kenya national football team in international competitions and records.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e517ebd48190979ee76723bcfadf completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e649f587d88190bf519fec7ce634d3 completed April 20, 2026, 3:44 p.m.
Created at: April 10, 2026, 1:46 p.m.