Triple

T1984713
Position Surface form Disambiguated ID Type / Status
Subject St. Louis Lambert International Airport E43114 entity
Predicate FAAcode P420 FINISHED
Object STL E34299 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: STL | Statement: [St. Louis Lambert International Airport, FAAcode, STL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: STL
Context triple: [St. Louis Lambert International Airport, FAAcode, STL]
  • A. STL chosen
    STL is a common abbreviation and nickname for the city of St. Louis, Missouri.
  • B. STLAM
    STLAM is the stock ticker symbol for Stellantis, a multinational automotive manufacturer formed from the merger of Fiat Chrysler Automobiles and PSA Group.
  • C. Effective STL
    Effective STL is a programming book by Scott Meyers that provides practical guidelines and best practices for using the C++ Standard Template Library effectively and efficiently.
  • D. C++
    C++ is a high-performance, general-purpose programming language widely used for system/software development, game engines, and performance-critical applications.
  • E. C++ standard library
    The C++ standard library is a collection of ready-made classes and functions that provide core utilities such as containers, algorithms, input/output, and threading support for C++ programs.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb821c2d48190abea6c89f37b51b1 completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0ad53ccc8190b0e0f44cfddfe9a4 completed March 8, 2026, 11:48 p.m.
Created at: March 4, 2026, 7:37 p.m.