Triple

T17351613
Position Surface form Disambiguated ID Type / Status
Subject Robert Malenka E421825 entity
Predicate hasPublishedIn P309 FINISHED
Object Science E53325 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: Science | Statement: [Robert Malenka, hasPublishedIn, Science]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Science
Context triple: [Robert Malenka, hasPublishedIn, Science]
  • A. Science chosen
    Science is a leading peer-reviewed academic journal that publishes cutting-edge research across a wide range of scientific disciplines.
  • B. Syience
    Syience is a music producer known for his work in contemporary R&B and pop, collaborating with prominent artists on charting tracks.
  • C. Nauka
    Nauka is a Russian-built multipurpose laboratory module on the International Space Station that provides research facilities, crew quarters, and additional propulsion and docking capabilities.
  • D. SCI
    SCI is the Small Carry-on Impactor, an explosive device on Japan’s Hayabusa2 spacecraft designed to create an artificial crater on asteroid Ryugu to expose and sample subsurface material.
  • E. SCI
    SCI is the abbreviation for the Strategic Computing Initiative, a U.S. Defense Advanced Research Projects Agency (DARPA) program from the 1980s that aimed to advance artificial intelligence and high-performance computing for military applications.
  • 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_69d889d520008190a26917a95bf1c2ea completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a2ca0708190aae8306ec3a6f2a7 completed April 19, 2026, 2:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0195585e5881909b0ad386b65112ba completed May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 5:44 a.m.