Triple

T6700143
Position Surface form Disambiguated ID Type / Status
Subject Clermont-Ferrand Auvergne Airport E152857 entity
Predicate ICAOcode P419 FINISHED
Object LFLC
LFLC is the ICAO airport code for Clermont-Ferrand Auvergne Airport in central France.
 E611254 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: LFLC | Statement: [Clermont-Ferrand Auvergne Airport, ICAOcode, LFLC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LFLC
Context triple: [Clermont-Ferrand Auvergne Airport, ICAOcode, LFLC]
  • A. LFL
    LFL is the Legends Football League, a women's American football league featuring teams such as the Chicago Bliss.
  • B. LFL
    LFL is the former New York Stock Exchange ticker symbol for LAN Airlines, a major Chilean airline that later became part of LATAM Airlines Group.
  • C. SFLC
    SFLC is a legal organization that provides pro bono counsel and advocacy to protect and advance free and open-source software.
  • D. FLK
    FLK is the ISO 3166-1 alpha-3 country code for the Falkland Islands, a British Overseas Territory in the South Atlantic Ocean.
  • E. LAF
    The Lebanese Armed Forces (LAF) are the military institution of Lebanon, responsible for defending the country’s sovereignty, maintaining internal security, and operating under a delicate sectarian balance.
  • 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: LFLC
Triple: [Clermont-Ferrand Auvergne Airport, ICAOcode, LFLC]
Generated description
LFLC is the ICAO airport code for Clermont-Ferrand Auvergne Airport in central France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LFLC
Target entity description: LFLC is the ICAO airport code for Clermont-Ferrand Auvergne Airport in central France.
  • A. LFL
    LFL is the Legends Football League, a women's American football league featuring teams such as the Chicago Bliss.
  • B. LFL
    LFL is the former New York Stock Exchange ticker symbol for LAN Airlines, a major Chilean airline that later became part of LATAM Airlines Group.
  • C. SFLC
    SFLC is a legal organization that provides pro bono counsel and advocacy to protect and advance free and open-source software.
  • D. FLK
    FLK is the ISO 3166-1 alpha-3 country code for the Falkland Islands, a British Overseas Territory in the South Atlantic Ocean.
  • E. LAF
    The Lebanese Armed Forces (LAF) are the military institution of Lebanon, responsible for defending the country’s sovereignty, maintaining internal security, and operating under a delicate sectarian balance.
  • 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_69c68807adbc8190b8632df42b39eda0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d0a7355081908a0acfa8d2bb4c09 completed March 27, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6f7bfcb048190b682f4ec7e404b3e completed March 27, 2026, 9:33 p.m.
NEDg Description generation batch_69c6f8955748819092b0e51cff6cab69 completed March 27, 2026, 9:37 p.m.
NED2 Entity disambiguation (via description) batch_69c6f945ee308190be1a830e394b5238 completed March 27, 2026, 9:40 p.m.
Created at: March 27, 2026, 2:05 p.m.