Triple

T8521616
Position Surface form Disambiguated ID Type / Status
Subject Room 101 E201704 entity
Predicate usedOnCharacter P2367 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: [Room 101, usedOnCharacter, Julia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Julia
Context triple: [Room 101, usedOnCharacter, Julia]
  • A. Julia
    "Julia" is a 1977 American drama film, based on Lillian Hellman’s memoir, that explores the intense lifelong friendship between a playwright and a woman involved in anti-fascist resistance before World War II.
  • B. Julia chosen
    Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
  • C. 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.
  • D. 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.
  • E. Rubinius
    Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
  • 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_69ca8321bb44819081b74df0b710276d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc4578d9c8819096b3853d01c3ec11 completed March 31, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e8399f481909992aedf0d918cbc completed April 2, 2026, 11:09 a.m.
Created at: March 30, 2026, 6:16 p.m.