Triple

T12517118
Position Surface form Disambiguated ID Type / Status
Subject msgcat E299215 entity
Predicate relatedTo P37 FINISHED
Object xgettext E299214 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: xgettext | Statement: [msgcat, relatedTo, xgettext]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: xgettext
Context triple: [msgcat, relatedTo, xgettext]
  • A. GNU gettext
    GNU gettext is a widely used GNU internationalization and localization framework that provides tools and libraries for translating the text of software programs into different languages.
  • B. libintl
    libintl is the core internationalization library from the GNU gettext system that provides runtime support for translating program messages into different languages.
  • C. GTrans
    GTrans is a public bus transit system serving the city of Gardena and surrounding areas in Los Angeles County, California.
  • D. textutils
    textutils was the former name of a collection of GNU command-line text processing utilities that were later consolidated into the GNU Core Utilities package.
  • E. msgmerge chosen
    msgmerge is a GNU gettext utility that updates translation files by intelligently merging new message catalogs with existing translations.
  • 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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9541f80148190976d1d912fe155d0 completed April 10, 2026, 7:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f64bbd58b88190baeb99380babf64f completed May 2, 2026, 7:08 p.m.
Created at: April 8, 2026, 9:57 p.m.