Triple

T16827932
Position Surface form Disambiguated ID Type / Status
Subject Dick Smith E409068 entity
Predicate notableWork P4 FINISHED
Object Scanners E415454 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: Scanners | Statement: [Dick Smith, notableWork, Scanners]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scanners
Context triple: [Dick Smith, notableWork, Scanners]
  • A. Scanners chosen
    Scanners is a 1981 science fiction horror film directed by David Cronenberg, best known for its intense psychic warfare and iconic head-explosion scene.
  • B. Odradek scanner
    The Odradek scanner is a multifunctional, rotating sensor unit in Death Stranding that helps Sam Porter Bridges detect terrain, threats, and cargo-related information in the environment.
  • C. MSU-E2 scanner
    The MSU-E2 scanner is a Russian multispectral Earth observation instrument used for remote sensing of the planet’s surface from orbit.
  • D. MSU-SK scanner
    The MSU-SK scanner is a Russian satellite-based multispectral scanning instrument used for Earth observation and environmental monitoring.
  • E. Autoscan
    Autoscan is a GNU Autotools utility that automatically analyzes a software package’s source tree to suggest a preliminary `configure.ac` script for portability checks.
  • 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_69d88394566c8190b3dcbdc72935f7fa completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b3151350819097b1c375e6df8986 completed April 18, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b2a0ac148190a7a7edebcb67c040 completed May 10, 2026, 4:30 p.m.
Created at: April 10, 2026, 5:23 a.m.