Triple

T2793048
Position Surface form Disambiguated ID Type / Status
Subject GNU gettext E61971 entity
Predicate includesTool P1393 FINISHED
Object msgmerge
msgmerge is a GNU gettext utility that updates translation files by intelligently merging new message catalogs with existing translations.
 E299214 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: msgmerge | Statement: [GNU gettext, includesTool, msgmerge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: msgmerge
Context triple: [GNU gettext, includesTool, msgmerge]
  • A. MLT
    MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
  • B. Translate extension
    Translate extension is a MediaWiki add-on that provides tools for managing and performing multilingual content translation within wikis.
  • C. mutter
    Mutter is a window manager and compositor for the X Window System and Wayland, best known as the core window management component of the GNOME desktop environment.
  • D. METS
    METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
  • E. TextEdit
    TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
  • 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: msgmerge
Triple: [GNU gettext, includesTool, msgmerge]
Generated description
msgmerge is a GNU gettext utility that updates translation files by intelligently merging new message catalogs with existing translations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: msgmerge
Target entity description: msgmerge is a GNU gettext utility that updates translation files by intelligently merging new message catalogs with existing translations.
  • A. MLT
    MLT is the three-letter ISO 3166-1 alpha-3 country code assigned to Malta.
  • B. Translate extension
    Translate extension is a MediaWiki add-on that provides tools for managing and performing multilingual content translation within wikis.
  • C. mutter
    Mutter is a window manager and compositor for the X Window System and Wayland, best known as the core window management component of the GNOME desktop environment.
  • D. METS
    METS (Metadata Encoding and Transmission Standard) is an XML-based standard for encoding descriptive, administrative, and structural metadata for complex digital library objects.
  • E. TextEdit
    TextEdit is a simple, built-in macOS application for creating and editing plain text and rich text documents.
  • 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_69ab4b7f51d881908768300ebd2fbdae completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abddd107ac81908eb1a6946834eee3 completed March 7, 2026, 8:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc65ebe788190859012e930918b05 completed March 10, 2026, 7:21 a.m.
NEDg Description generation batch_69afc6c6c620819098b76db174a6f98e completed March 10, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69afc72b2e3c8190aad78ac8924f07af completed March 10, 2026, 7:24 a.m.
Created at: March 6, 2026, 9:58 p.m.