Triple

T2704845
Position Surface form Disambiguated ID Type / Status
Subject Imaging Science System E59317 entity
Predicate capturedImagesOf P37258 FINISHED
Object Miranda E117408 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: Miranda | Statement: [Imaging Science System, capturedImagesOf, Miranda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Miranda
Context triple: [Imaging Science System, capturedImagesOf, Miranda]
  • A. Miranda
    Miranda is a common Spanish-origin surname shared by numerous notable individuals across the arts, politics, and other fields.
  • B. Miranda chosen
    Miranda is one of Uranus's major moons, known for its unusually varied and geologically complex surface featuring dramatic cliffs and patchwork terrains.
  • C. Querença
    Querença is a traditional rural village in Portugal’s Algarve region, known for its whitewashed houses, natural springs, and cultural festivals.
  • D. Karla
    Karla is the elusive Soviet spymaster and primary antagonist of John le Carré’s George Smiley novels, symbolizing the Cold War espionage rivalry between British intelligence and the KGB.
  • E. Dimona
    Dimona is a town in southern Israel best known for its proximity to the Negev Nuclear Research Center and its role in the development of the Negev desert region.
  • 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_69ab4ac66bc88190b9e4afa5fc843f72 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abdd1fc30c81909ac06588d50abdf8 completed March 7, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf79c8648190a9041dd2903a1429 completed March 10, 2026, 5:43 a.m.
Created at: March 6, 2026, 9:55 p.m.