Triple
T2629544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | gcov |
E59599
|
entity |
| Predicate | relatedTool |
P28584
|
FINISHED |
| Object |
lcov
lcov is a graphical front-end and extension for the gcov code coverage tool that collects, processes, and visualizes test coverage data for C and C++ programs.
|
E284618
|
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: lcov | Statement: [gcov, relatedTool, lcov]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: lcov Context triple: [gcov, relatedTool, lcov]
-
A.
gcov
gcov is a test coverage analysis tool used with GCC to measure and report how much of a program’s source code is executed during runtime.
-
B.
Checkmarx
Checkmarx is a cybersecurity company specializing in application security testing solutions that help organizations identify and remediate vulnerabilities in their software code.
-
C.
LLVM
LLVM is a modular, reusable compiler and toolchain infrastructure project widely used for building language frontends, optimizers, and backends for diverse hardware architectures.
-
D.
gprof
gprof is a performance analysis tool that profiles program execution to help developers identify time-consuming functions and optimize their code.
-
E.
LLDB
LLDB is a modern, high-performance debugger primarily used with the LLVM toolchain for languages like C, C++, and Objective-C.
- 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: lcov Triple: [gcov, relatedTool, lcov]
Generated description
lcov is a graphical front-end and extension for the gcov code coverage tool that collects, processes, and visualizes test coverage data for C and C++ programs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: lcov Target entity description: lcov is a graphical front-end and extension for the gcov code coverage tool that collects, processes, and visualizes test coverage data for C and C++ programs.
-
A.
gcov
gcov is a test coverage analysis tool used with GCC to measure and report how much of a program’s source code is executed during runtime.
-
B.
Checkmarx
Checkmarx is a cybersecurity company specializing in application security testing solutions that help organizations identify and remediate vulnerabilities in their software code.
-
C.
LLVM
LLVM is a modular, reusable compiler and toolchain infrastructure project widely used for building language frontends, optimizers, and backends for diverse hardware architectures.
-
D.
gprof
gprof is a performance analysis tool that profiles program execution to help developers identify time-consuming functions and optimize their code.
-
E.
LLDB
LLDB is a modern, high-performance debugger primarily used with the LLVM toolchain for languages like C, C++, and Objective-C.
- 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_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. |
| NEDg | Description generation | batch_69af9172ba248190bbc68a00b43d9b44 |
completed | March 10, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69af92500920819082c651f75a06dd72 |
completed | March 10, 2026, 3:38 a.m. |
Created at: March 6, 2026, 9:50 p.m.