Triple

T9743284
Position Surface form Disambiguated ID Type / Status
Subject Einstein’s 1907 equivalence principle paper E236242 entity
Predicate author P4 FINISHED
Object Albert Einstein E318 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: Albert Einstein | Statement: [Einstein’s 1907 equivalence principle paper, author, Albert Einstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Albert Einstein
Context triple: [Einstein’s 1907 equivalence principle paper, author, Albert Einstein]
  • A. Albert Einstein chosen
    Albert Einstein was a theoretical physicist best known for developing the theory of relativity and fundamentally reshaping modern physics.
  • B. Einstein
    Einstein is a renowned surname most famously associated with theoretical physicist Albert Einstein, whose work revolutionized modern physics.
  • C. Einstein
    Einstein is the time-traveling sheepdog from the Back to the Future film series, known as Doc Brown’s beloved canine companion and the first test subject of the DeLorean time machine.
  • D. Thomas Einstein
    Thomas Einstein is a physician and anesthesiologist known for being a great-grandson of physicist Albert Einstein.
  • E. EINSTEIN
    EINSTEIN is a U.S. federal intrusion detection and prevention system used to monitor and protect government agency networks from cyber threats.
  • 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_69ca84d3e24481908a476e2231123cf9 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f2e5bb081908047e3bf5fe3991c completed April 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1affb761881908dde9a4f028c32f6 completed April 5, 2026, 12:42 a.m.
Created at: March 30, 2026, 8:23 p.m.