Triple

T2923520
Position Surface form Disambiguated ID Type / Status
Subject Endeavor Air E78787 entity
Predicate ICAOCode P419 FINISHED
Object FLG
FLG is the ICAO airline designator used to identify Endeavor Air in international aviation operations.
 E309549 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: FLG | Statement: [Endeavor Air, ICAOCode, FLG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: FLG
Context triple: [Endeavor Air, ICAOCode, FLG]
  • A. Flagg
    Flagg is a surname most notably associated with American artist and illustrator James Montgomery Flagg, famed for creating the iconic "I Want YOU for U.S. Army" Uncle Sam poster.
  • B. LFPG
    LFPG is the ICAO airport code for Paris Charles de Gaulle Airport, France’s largest and busiest international air hub.
  • C. FLAR
    FLAR is a regional financial organization that provides balance-of-payments support, reserve pooling, and financial stability assistance to its Latin American member countries.
  • D. FALA
    FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
  • E. FMG
    FMG is the Faculty of Social and Behavioural Sciences at the University of Amsterdam, encompassing disciplines such as psychology, sociology, political science, and communication science.
  • 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: FLG
Triple: [Endeavor Air, ICAOCode, FLG]
Generated description
FLG is the ICAO airline designator used to identify Endeavor Air in international aviation operations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: FLG
Target entity description: FLG is the ICAO airline designator used to identify Endeavor Air in international aviation operations.
  • A. Flagg
    Flagg is a surname most notably associated with American artist and illustrator James Montgomery Flagg, famed for creating the iconic "I Want YOU for U.S. Army" Uncle Sam poster.
  • B. LFPG
    LFPG is the ICAO airport code for Paris Charles de Gaulle Airport, France’s largest and busiest international air hub.
  • C. FLAR
    FLAR is a regional financial organization that provides balance-of-payments support, reserve pooling, and financial stability assistance to its Latin American member countries.
  • D. FALA
    FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
  • E. FMG
    FMG is the Faculty of Social and Behavioural Sciences at the University of Amsterdam, encompassing disciplines such as psychology, sociology, political science, and communication science.
  • 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_69ad8b0c2ad081909ff87050ae542bb9 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad97bf2df88190bd4f1e90d4656507 completed March 8, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69b056375b5c819081c7d4fd506cbf25 completed March 10, 2026, 5:34 p.m.
NEDg Description generation batch_69b05f640afc8190bf9b5b90ff7c9b0e completed March 10, 2026, 6:13 p.m.
NED2 Entity disambiguation (via description) batch_69b06010c1948190a2e13084a79b106b completed March 10, 2026, 6:16 p.m.
Created at: March 8, 2026, 2:55 p.m.