Triple
T815657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruby |
E17647
|
entity |
| Predicate | hasMajorImplementation |
P16200
|
FINISHED |
| Object |
Rubinius
Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
|
E96203
|
NE FINISHED |
How this triple was built (4 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: Rubinius | Statement: [Ruby, hasMajorImplementation, Rubinius]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rubinius Context triple: [Ruby, hasMajorImplementation, Rubinius]
-
A.
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.
-
B.
Julia
Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
C.
Guido
Guido is a masculine given name of Italian origin, famously borne by Guido van Rossum, the creator of the Python programming language.
-
D.
Elm
Elm is a civil parish and village in Cambridgeshire, England, known for its rural character and historic church.
-
E.
Elm
Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Rubinius Triple: [Ruby, hasMajorImplementation, Rubinius]
Generated description
Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rubinius Target entity description: Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
-
A.
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.
-
B.
Julia
Julia is a feminine given name of Latin origin, commonly used in many languages and cultures.
-
C.
Guido
Guido is a masculine given name of Italian origin, famously borne by Guido van Rossum, the creator of the Python programming language.
-
D.
Elm
Elm is a statically typed, functional programming language that compiles to JavaScript and is designed for building reliable, maintainable web front-end applications.
-
E.
Elm
Elm is a civil parish and village in Cambridgeshire, England, known for its rural character and historic church.
- F. None of above. chosen
Provenance (5 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4b2b503d48190bd4f33548a22d5fe |
completed | March 1, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d8b0b0c8190a6226d6b8daade25 |
completed | March 3, 2026, 11:23 p.m. |
| NEDg | Description generation | batch_69a782eda49c8190bdaf4fb8db685071 |
completed | March 4, 2026, 12:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a784f6eee48190a348008b931d545b |
completed | March 4, 2026, 1:03 a.m. |
Created at: March 1, 2026, 7:38 p.m.