Triple

T18808743
Position Surface form Disambiguated ID Type / Status
Subject CemAir E459947 entity
Predicate callsign P1565 FINISHED
Object CEMAIR 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: CEMAIR | Statement: [CemAir, callsign, CEMAIR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CEMAIR
Context triple: [CemAir, callsign, CEMAIR]
  • A. CEMA
    CEMA is the acronym for the French Chief of the Defence Staff, the highest-ranking military officer overseeing France’s armed forces.
  • B. CemAir chosen
    CemAir is a South African regional and domestic airline operating scheduled and charter flights to various destinations within South Africa and neighboring countries.
  • C. CEMO
    CEMO is a research center focused on advancing educational measurement, assessment, and related methodologies.
  • D. European Civil Aviation Conference
    The European Civil Aviation Conference is an intergovernmental organization that coordinates civil aviation policies and regulations among European states to promote safe, efficient, and harmonized air transport.
  • E. COMESSA
    COMESSA is the French acronym for the Community of Sahel-Saharan States, a regional organization focused on economic integration and cooperation among countries in the Sahel and Sahara regions of Africa.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a3d9c49c8190a9d29a25f0c977b2 completed April 20, 2026, 3:56 a.m.
Created at: April 10, 2026, 11:53 a.m.