Triple

T1831241
Position Surface form Disambiguated ID Type / Status
Subject Sugar Labs E40765 entity
Predicate name P16 FINISHED
Object Sugar Labs E40765 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: Sugar Labs | Statement: [Sugar Labs, name, Sugar Labs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sugar Labs
Context triple: [Sugar Labs, name, Sugar Labs]
  • A. Sugar Labs chosen
    Sugar Labs is a nonprofit organization that develops and maintains the Sugar learning platform, an open-source educational software environment originally created for the One Laptop per Child project.
  • B. JoyLabz
    JoyLabz is the company best known for creating the Makey Makey invention kit, which turns everyday objects into touchpads for creative, educational electronics projects.
  • C. Obvious Corporation
    Obvious Corporation was the early holding company and development vehicle created by Twitter’s founders that incubated and spun out Twitter as an independent company.
  • D. Pyra Labs
    Pyra Labs is the software company best known for creating Blogger, one of the earliest and most influential web-based blogging platforms.
  • E. Founders Lab
    Founders Lab is an innovation and entrepreneurship space at Elmhurst University that supports student startups and experiential learning in business and technology.
  • 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_69a8864644bc8190b2358ab897194ac1 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb022aef48190975b6d12fc6681ad completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adbf6f212c8190b0d182f8486d9e47 completed March 8, 2026, 6:26 p.m.
Created at: March 4, 2026, 7:32 p.m.