Triple
T12900255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MIT AI Lab software environment |
E308593
|
entity |
| Predicate | supportedProgrammingLanguage |
P83124
|
FINISHED |
| Object | Maclisp |
E146667
|
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: Maclisp | Statement: [MIT AI Lab software environment, supportedProgrammingLanguage, Maclisp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maclisp Context triple: [MIT AI Lab software environment, supportedProgrammingLanguage, Maclisp]
-
A.
Maclisp
chosen
Maclisp is an early and influential dialect of the Lisp programming language developed at MIT, notable for shaping later Lisp systems and language designs.
-
B.
Interlisp
Interlisp was an early, influential dialect and programming environment of the Lisp language, notable for its integrated development tools and impact on later Lisp systems.
-
C.
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.
-
D.
Lisp Machine Lisp
Lisp Machine Lisp is a dialect of the Lisp programming language developed for specialized Lisp machine hardware, notable for its rich object system and tight integration with the underlying operating environment.
-
E.
Chez Scheme
Chez Scheme is a high-performance, optimizing implementation of the Scheme programming language widely used for both research and production systems.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97dc53060819090a126f15428e411 |
completed | April 10, 2026, 10:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a56189b081909ed838addcb6d265 |
completed | May 3, 2026, 1:31 a.m. |
Created at: April 9, 2026, 5:40 p.m.