Triple
T5923867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ML |
E131757
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | ReasonML |
E24477
|
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: ReasonML | Statement: [ML, influenced, ReasonML]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ReasonML Context triple: [ML, influenced, ReasonML]
-
A.
ReasonML
chosen
ReasonML is a syntax and toolchain for the OCaml language that offers a JavaScript-friendly, type-safe alternative for building web and native applications.
-
B.
OCaml
OCaml is a statically typed functional programming language from the ML family, known for its powerful type system, pattern matching, and efficient native code compilation.
-
C.
BuckleScript
BuckleScript is a JavaScript backend and toolchain that compiles OCaml/ReasonML code into highly optimized, readable JavaScript for web and Node.js development.
-
D.
Coq
Coq is an interactive theorem prover and functional programming language based on dependent type theory, widely used for formally verifying mathematical proofs and software correctness.
-
E.
Standard ML
Standard ML is a statically typed functional programming language with type inference and a formal semantics, widely used in programming language research and teaching.
- 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_69c0085a1ed08190a7e9a8b6323fd680 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03851189c819094524e8b5080545e |
completed | March 22, 2026, 6:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c0483e3481908e50f8b34b11a878 |
completed | March 23, 2026, 4:23 a.m. |
Created at: March 22, 2026, 4 p.m.