Triple
T6009892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Common Lisp |
E133804
|
entity |
| Predicate | hasImplementation |
P3697
|
FINISHED |
| Object | CMUCL |
E474906
|
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: CMUCL | Statement: [Common Lisp, hasImplementation, CMUCL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CMUCL Context triple: [Common Lisp, hasImplementation, CMUCL]
-
A.
CMU Common Lisp
chosen
CMU Common Lisp is a high-performance, open-source implementation of the Common Lisp programming language developed at Carnegie Mellon University, notable for its advanced compiler and optimization capabilities.
-
B.
Franz Lisp
Franz Lisp is a dialect of the Lisp programming language developed in the late 1970s at the University of California, Berkeley, primarily for use in artificial intelligence research and symbolic computation.
-
C.
CMU
CMU is a public university in Grand Junction, Colorado, known for its diverse undergraduate programs and strong regional presence on the Western Slope.
-
D.
CMU
CMU is a private research university in Pittsburgh, Pennsylvania, renowned for its leading programs in computer science, engineering, and the arts.
-
E.
CMU
CMU is a major medical university located in Shenyang, China, known for its education and research in clinical medicine and related health sciences.
- 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_69c0087361a48190905c6b55969852b8 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f4e27a881909cc3f7fef62abc3b |
completed | March 22, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1089bd870819096c0f6c7cf484c50 |
completed | March 23, 2026, 9:32 a.m. |
Created at: March 22, 2026, 4:06 p.m.