Triple
T4443385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Random |
E96222
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | StatsBase.jl |
E17648
|
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: StatsBase.jl | Statement: [Random, usedWith, StatsBase.jl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: StatsBase.jl Context triple: [Random, usedWith, StatsBase.jl]
-
A.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
-
B.
Julia
Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
C.
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.
-
D.
Julia
chosen
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.
-
E.
Statistics
Statistics is a Julia standard library module that provides basic statistical functions such as means, variances, and related summary measures for numerical data.
- 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_69b345415ba481908df738e7174448ba |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355b052688190a0d8e5912f82151c |
completed | March 13, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b61382d00481908b7c84f337b5cad7 |
completed | March 15, 2026, 2:03 a.m. |
Created at: March 12, 2026, 11:32 p.m.