Triple
T2629298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fortran |
E59594
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | Julia |
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: Julia | Statement: [Fortran, influenced, Julia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julia Context triple: [Fortran, influenced, Julia]
-
A.
Julia
Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
B.
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.
-
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.
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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8c452508190b02e1630d725497a |
completed | March 7, 2026, 7:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af90a44a348190b8b49b37418dd94b |
completed | March 10, 2026, 3:31 a.m. |
Created at: March 6, 2026, 9:50 p.m.