Triple
T815780
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Go |
E17649
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Golang |
E17649
|
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: Golang | Statement: [Go, alsoKnownAs, Golang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Golang Context triple: [Go, alsoKnownAs, Golang]
-
A.
Go
chosen
Go is a statically typed, compiled programming language developed at Google, known for its simplicity, efficient concurrency support, and suitability for scalable networked and cloud services.
-
B.
Rust
Rust is a modern systems programming language focused on memory safety, concurrency, and performance without a garbage collector.
-
C.
Dart
Dart is a client-optimized, object-oriented programming language developed by Google, primarily used for building web and cross-platform mobile applications (notably with the Flutter framework).
-
D.
Deno
Deno is a modern, secure JavaScript and TypeScript runtime created by Ryan Dahl as a successor to Node.js, featuring built-in TypeScript support and a permission-based security model.
-
E.
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.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab5157b08190b6c8f2fd455f261e |
completed | March 1, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d8b0b0c8190a6226d6b8daade25 |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:38 p.m.