Triple
T2889533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XEmacs |
E59584
|
entity |
| Predicate | supportsLanguage |
P2177
|
FINISHED |
| Object | Lisp |
E94990
|
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: Lisp | Statement: [XEmacs, supportsLanguage, Lisp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lisp Context triple: [XEmacs, supportsLanguage, Lisp]
-
A.
Lisp programming language
chosen
Lisp is a pioneering high-level programming language, especially influential in artificial intelligence research and known for its symbolic processing and distinctive parenthesized syntax.
-
B.
Common Lisp
Common Lisp is a powerful, multi-paradigm dialect of the Lisp programming language standardised in the 1980s, known for its rich macro system, dynamic typing, and suitability for large-scale, extensible software systems.
-
C.
Scheme
Scheme is a minimalist, lexically scoped dialect of the Lisp programming language known for its elegant functional programming model and powerful macro system.
-
D.
Maclisp
Maclisp is an early and influential dialect of the Lisp programming language developed at MIT, notable for shaping later Lisp systems and language designs.
-
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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe04a68ac8190aaeafe52138beb74 |
completed | March 7, 2026, 8:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03179d7448190bcdbea164856aaa2 |
completed | March 10, 2026, 2:58 p.m. |
Created at: March 6, 2026, 10:04 p.m.