Triple

T2216612
Position Surface form Disambiguated ID Type / Status
Subject Walter Murch E48046 entity
Predicate notableWork P4 FINISHED
Object Julia E92860 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: Julia | Statement: [Walter Murch, notableWork, Julia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Julia
Context triple: [Walter Murch, notableWork, Julia]
  • A. Julia chosen
    Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
  • B. Julia
    Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
  • C. gens Julia
    The gens Julia was one of ancient Rome’s most prominent patrician families, traditionally claiming descent from the Trojan hero Aeneas and including figures such as Julius Caesar and Augustus.
  • D. Rubinius
    Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
  • E. Elixir
    Elixir is a functional, concurrent programming language built on the Erlang VM, known for its scalability, fault tolerance, and expressive syntax.
  • 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_69a88aa1ee708190862c8c378c41e9eb completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc00f4c3881909d03301fcdfa8b67 completed March 7, 2026, 6:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae65594c948190a43bdab03e61c130 completed March 9, 2026, 6:14 a.m.
Created at: March 4, 2026, 7:46 p.m.