Triple

T13809630
Position Surface form Disambiguated ID Type / Status
Subject Nokia E6 E331851 entity
Predicate hasFeature P182 FINISHED
Object GPS E26827 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: GPS | Statement: [Nokia E6, hasFeature, GPS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: GPS
Context triple: [Nokia E6, hasFeature, GPS]
  • A. GPS chosen
    GPS (Global Positioning System) is a satellite-based navigation system that provides precise location and timing information to military and civilian users worldwide.
  • B. GPS
    GPS is the Division of Geological and Planetary Sciences at the California Institute of Technology, focusing on research and education in Earth and planetary sciences.
  • C. GPS
    GPS is a professional school at the University of California San Diego specializing in international affairs, public policy, and global strategy.
  • D. GPS
    GPS is an early artificial intelligence program developed in the late 1950s to model human problem-solving by systematically searching for solutions in a defined problem space.
  • E. Differential GPS
    Differential GPS is an enhanced positioning system that improves the accuracy of standard GPS signals by using ground-based reference stations to correct signal errors.
  • 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_69d81c59f8808190a851bc56afdc55e9 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de026ff6b481908066d6bf27064417 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b08fbc348190a199c5d92e0e46be completed May 3, 2026, 8:31 p.m.
Created at: April 9, 2026, 10:12 p.m.