Triple

T1928064
Position Surface form Disambiguated ID Type / Status
Subject All Nippon Airways E40877 entity
Predicate ICAOcode P419 FINISHED
Object ANA
ANA is the ICAO airline designator for All Nippon Airways, Japan’s largest airline and a major global carrier.
 E218076 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: ANA | Statement: [All Nippon Airways, ICAOcode, ANA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ANA
Context triple: [All Nippon Airways, ICAOcode, ANA]
  • A. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • B. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • C. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • D. AN
    AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
  • E. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • 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: ANA
Triple: [All Nippon Airways, ICAOcode, ANA]
Generated description
ANA is the ICAO airline designator for All Nippon Airways, Japan’s largest airline and a major global carrier.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ANA
Target entity description: ANA is the ICAO airline designator for All Nippon Airways, Japan’s largest airline and a major global carrier.
  • A. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • B. ANA
    ANA is the commonly used abbreviation for the Afghan National Army, the former main land warfare branch of Afghanistan’s armed forces.
  • C. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • D. AN
    AN is the vehicle registration code used on license plates for the Ansbach district in the Middle Franconia region of Bavaria, Germany.
  • E. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • 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_69a8864711648190b07bed24ed76258e completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb263cdb8819084d0bda98a2a71e0 completed March 7, 2026, 5:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfbb2cc988190a4046c17c8389996 completed March 8, 2026, 10:44 p.m.
NEDg Description generation batch_69adfc6aa96c81909ae3cff6c7ab7f79 completed March 8, 2026, 10:47 p.m.
NED2 Entity disambiguation (via description) batch_69adfcebbc808190a74f9082636bce11 completed March 8, 2026, 10:49 p.m.
Created at: March 4, 2026, 7:35 p.m.