Triple

T10412331
Position Surface form Disambiguated ID Type / Status
Subject CA E245422 entity
Predicate assignedTo P3151 FINISHED
Object Air China E48734 NE FINISHED

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: Air China | Statement: [CA, assignedTo, Air China]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Air China
Context triple: [CA, assignedTo, Air China]
  • A. Air China chosen
    Air China is the flag carrier and one of the largest airlines of the People's Republic of China, operating extensive domestic and international passenger and cargo services.
  • B. China Southern Airlines
    China Southern Airlines is one of China’s largest airlines, operating extensive domestic and international routes from major hubs like Guangzhou and Beijing.
  • C. Juneyao Air
    Juneyao Air is a Chinese full-service airline based in Shanghai that operates domestic and international flights, primarily across Asia and to select long-haul destinations.
  • D. Shanghai Airlines
    Shanghai Airlines is a Chinese airline based in Shanghai that operates domestic and international passenger flights as a subsidiary of China Eastern Airlines.
  • E. China National Aviation Corporation
    China National Aviation Corporation was a major Chinese airline and air transport company that operated both civilian and military-related flights, particularly prominent during the mid-20th century.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d381be340c8190b05998703d42d224 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea0e17f081908fb16425f65e5808 completed April 7, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7fbfae96c8190ac496e9a1158afe4 completed April 9, 2026, 7:20 p.m.
Created at: April 6, 2026, 12:10 p.m.