Triple

T497681
Position Surface form Disambiguated ID Type / Status
Subject GNU Project E10329 entity
Predicate hasPart P35 FINISHED
Object 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.
 E61971 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: GNU gettext | Statement: [GNU Project, hasPart, GNU gettext]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GNU gettext
Context triple: [GNU Project, hasPart, GNU gettext]
  • A. Unicode Consortium
    The Unicode Consortium is a non-profit organization that standardizes the representation of text and symbols in digital systems worldwide through the Unicode Standard.
  • B. GNU Project
    The GNU Project is a free software initiative that created many core components of the GNU/Linux operating system and pioneered the modern free software movement.
  • C. Unicode Technical Report #29
    Unicode Technical Report #29 is the specification that defines how to determine and segment user-perceived text elements (grapheme clusters), words, and sentences in Unicode text.
  • D. Unicode Technical Standard #10
    Unicode Technical Standard #10 is the specification that defines the Unicode Collation Algorithm, providing a standardized method for comparing and sorting Unicode text across languages and platforms.
  • E. Google Translate
    Google Translate is a multilingual neural machine translation service by Google that instantly converts text, speech, images, and web pages between numerous languages.
  • 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: GNU gettext
Triple: [GNU Project, hasPart, GNU gettext]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GNU gettext
Target entity description: 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.
  • A. Unicode Consortium
    The Unicode Consortium is a non-profit organization that standardizes the representation of text and symbols in digital systems worldwide through the Unicode Standard.
  • B. GNU Project
    The GNU Project is a free software initiative that created many core components of the GNU/Linux operating system and pioneered the modern free software movement.
  • C. Unicode Technical Report #29
    Unicode Technical Report #29 is the specification that defines how to determine and segment user-perceived text elements (grapheme clusters), words, and sentences in Unicode text.
  • D. Unicode Technical Standard #10
    Unicode Technical Standard #10 is the specification that defines the Unicode Collation Algorithm, providing a standardized method for comparing and sorting Unicode text across languages and platforms.
  • E. Google Translate
    Google Translate is a multilingual neural machine translation service by Google that instantly converts text, speech, images, and web pages between numerous languages.
  • 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_69a2e847df8481909239ec08ccf1e376 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1183e988190bce70932a9678134 completed Feb. 28, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69a481efc3a881909575c5981e13a16b completed March 1, 2026, 6:14 p.m.
NEDg Description generation batch_69a48352d91481909fb2119752595927 completed March 1, 2026, 6:20 p.m.
NED2 Entity disambiguation (via description) batch_69a483bb51c48190a0c2d9a3198e0ae2 completed March 1, 2026, 6:21 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.