Triple

T264664
Position Surface form Disambiguated ID Type / Status
Subject GitHub E5697 entity
Predicate hasService P182 FINISHED
Object Gist
Gist is GitHub’s lightweight code snippet and file-sharing service that lets users quickly create, share, and version small pieces of code or text.
 E34617 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: Gist | Statement: [GitHub, hasService, Gist]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gist
Context triple: [GitHub, hasService, Gist]
  • A. Grok
    Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
  • B. GOC
    GOC is the standardised set of spelling and writing rules used for modern Scottish Gaelic.
  • C. Gumm
    Gumm is the birth surname of American actress and singer Judy Garland, originally Frances Ethel Gumm.
  • D. Geeks Bearing Gifts
    Geeks Bearing Gifts is a book by computing pioneer Ted Nelson that reflects on the history, philosophy, and future of digital media and information technology.
  • E. The Example
    The Example is a 17th-century stage comedy by English dramatist James Shirley, reflecting the manners and social intrigues of Caroline-era London.
  • 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: Gist
Triple: [GitHub, hasService, Gist]
Generated description
Gist is GitHub’s lightweight code snippet and file-sharing service that lets users quickly create, share, and version small pieces of code or text.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gist
Target entity description: Gist is GitHub’s lightweight code snippet and file-sharing service that lets users quickly create, share, and version small pieces of code or text.
  • A. Grok
    Grok is an AI chatbot developed by xAI, designed to provide conversational access to real-time information and reasoning capabilities.
  • B. GOC
    GOC is the standardised set of spelling and writing rules used for modern Scottish Gaelic.
  • C. Gumm
    Gumm is the birth surname of American actress and singer Judy Garland, originally Frances Ethel Gumm.
  • D. Geeks Bearing Gifts
    Geeks Bearing Gifts is a book by computing pioneer Ted Nelson that reflects on the history, philosophy, and future of digital media and information technology.
  • E. The Example
    The Example is a 17th-century stage comedy by English dramatist James Shirley, reflecting the manners and social intrigues of Caroline-era London.
  • 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_69a2587daeb081909591b9d30f80a271 completed Feb. 28, 2026, 2:52 a.m.
NER Named-entity recognition batch_69a25d8f9bbc8190a13841e4de093a66 completed Feb. 28, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69a389ae45648190966804664bf4f861 completed March 1, 2026, 12:34 a.m.
NEDg Description generation batch_69a38a9adf4481909300cfa1fb3129cc completed March 1, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_69a38aef72808190953043b9c480fada completed March 1, 2026, 12:40 a.m.
Created at: Feb. 28, 2026, 2:56 a.m.