Triple
T11959458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CMake |
E284629
|
entity |
| Predicate | maintainer |
P2962
|
FINISHED |
| Object | Kitware |
E956222
|
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: Kitware | Statement: [CMake, maintainer, Kitware]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kitware Context triple: [CMake, maintainer, Kitware]
-
A.
Kitware
chosen
Kitware is a software research and development company best known for creating open-source tools and platforms for scientific computing, visualization, and software build management.
-
B.
Inprise Corporation
Inprise Corporation was the temporary name used by software company Borland during a late-1990s rebranding focused on enterprise solutions.
-
C.
Eiffel Software
Eiffel Software is a software company best known for developing the Eiffel programming language and tools that emphasize object-oriented design and software reliability.
-
D.
The Qt Company
The Qt Company is a Finnish software company best known for developing and maintaining the Qt cross-platform application and user interface framework used worldwide in desktop, mobile, and embedded systems.
-
E.
Codership
Codership is a software company best known for creating the Galera Cluster synchronous multi-master replication technology for MySQL and related databases.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9036941948190b150369094551731 |
completed | April 10, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f471d625c88190baed4ea08853988a |
completed | May 1, 2026, 9:26 a.m. |
Created at: April 8, 2026, 9:45 p.m.