Triple

T22444067
Position Surface form Disambiguated ID Type / Status
Subject LoopBack E554821 entity
Predicate version P3286 FINISHED
Object LoopBack 3 NE NERFINISHED

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: LoopBack 3 | Statement: [LoopBack, version, LoopBack 3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: LoopBack 3
Context triple: [LoopBack, version, LoopBack 3]
  • A. LoopBack chosen
    LoopBack is an open-source Node.js framework for building APIs and connecting them to backend data sources.
  • B. StrongLoop
    StrongLoop is a software company best known for its work on Node.js tools and frameworks, including previously maintaining the popular Express.js web application framework.
  • C. Loop 303
    Loop 303 is a major freeway in the Phoenix metropolitan area that serves as part of the region’s outer beltway system, facilitating circumferential travel and suburban growth.
  • D. Loop 101
    Loop 101 is a major freeway encircling much of the Phoenix metropolitan area, connecting numerous suburbs and serving as a key route for regional traffic.
  • E. Loop 336
    Loop 336 is a major circumferential roadway that serves as a key traffic artery around the city of Conroe, Texas.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e5113208190ab58c6b595f9d1d0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15ae517208190924a7968723f55ef completed April 29, 2026, 1:12 a.m.
Created at: April 16, 2026, 8:47 p.m.