Triple

T9899931
Position Surface form Disambiguated ID Type / Status
Subject Microsoft Foundation Classes E182257 entity
Predicate platform P1292 FINISHED
Object Win32 E37357 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: Win32 | Statement: [Microsoft Foundation Classes, platform, Win32]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Win32
Context triple: [Microsoft Foundation Classes, platform, Win32]
  • A. Win32 API chosen
    Win32 API is Microsoft's core programming interface for developing native desktop applications that interact directly with the Windows operating system.
  • B. User32
    User32 is a core Windows API library that manages user interface components such as windows, menus, and input handling.
  • C. Windows SDK
    Windows SDK is Microsoft's official collection of tools, headers, libraries, and documentation used by developers to build applications for the Windows operating system.
  • D. Kernel32
    Kernel32 is a core Windows system library that provides fundamental kernel-level functions for process, memory, and thread management in the Win32 API.
  • E. Windows/386
    Windows/386 was a special edition of Microsoft Windows 2.x designed to take advantage of Intel 80386 processors, offering enhanced multitasking and memory management capabilities.
  • 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_69ca82876f8081909cf75df0f99bb13f completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cdb4e0705c8190bd17e36aff615cdd completed April 2, 2026, 12:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1eb1b9534819093c5150f1ed8f685 completed April 5, 2026, 4:54 a.m.
Created at: March 30, 2026, 8:40 p.m.