Triple

T805105
Position Surface form Disambiguated ID Type / Status
Subject Codex E17412 entity
Predicate supportsLanguage P2177 FINISHED
Object Lua
Lua is a lightweight, embeddable scripting language widely used for game development, configuration, and extending applications.
 E95187 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: Lua | Statement: [Codex, supportsLanguage, Lua]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lua
Context triple: [Codex, supportsLanguage, Lua]
  • A. Elm
    Elm is a civil parish and village in Cambridgeshire, England, known for its rural character and historic church.
  • B. Elm
    Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
  • C. Julia
    Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
  • D. Julia
    Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
  • E. GML
    GML (Geography Markup Language) is an XML-based standard developed by the Open Geospatial Consortium for modeling, transporting, and storing geographic information and spatial features.
  • 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: Lua
Triple: [Codex, supportsLanguage, Lua]
Generated description
Lua is a lightweight, embeddable scripting language widely used for game development, configuration, and extending applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lua
Target entity description: Lua is a lightweight, embeddable scripting language widely used for game development, configuration, and extending applications.
  • A. Elm
    Elm is a civil parish and village in Cambridgeshire, England, known for its rural character and historic church.
  • B. Elm
    Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
  • C. Julia
    Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
  • D. Julia
    Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
  • E. GML
    GML (Geography Markup Language) is an XML-based standard developed by the Open Geospatial Consortium for modeling, transporting, and storing geographic information and spatial features.
  • 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_69a4937ae8a08190b5084a03d532b30e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aabff3d88190bec4299fa0d87df0 completed March 1, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69a68926c04081908923a7d114d1842d completed March 3, 2026, 7:09 a.m.
NEDg Description generation batch_69a693cf5f348190868cdf3539274aeb completed March 3, 2026, 7:54 a.m.
NED2 Entity disambiguation (via description) batch_69a6d5bc74008190b94ef7ea63f39671 completed March 3, 2026, 12:36 p.m.
Created at: March 1, 2026, 7:38 p.m.