Triple
T22445412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R^nRS |
E554850
|
entity |
| Predicate | hasAbbreviation |
P43
|
FINISHED |
| Object |
RnRS
RnRS is the standard specification document series that formally defines the Scheme programming language.
|
E1537648
|
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: RnRS | Statement: [R^nRS, hasAbbreviation, RnRS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RnRS Context triple: [R^nRS, hasAbbreviation, RnRS]
-
A.
R6RS
R6RS is the sixth revision of the Scheme programming language standard, defining its core language features, libraries, and semantics.
-
B.
R4RS
R4RS is the fourth revised report on the Scheme programming language standard, defining its core syntax, semantics, and standard procedures.
-
C.
R7RS (small) (partial)
R7RS (small) (partial) is a subset of the Revised⁷ Report on the Algorithmic Language Scheme standard that defines a core, lightweight version of the Scheme programming language.
-
D.
Scheme R5RS
Scheme R5RS is the fifth revised report of the Scheme programming language standard, defining its core syntax, semantics, and standard libraries.
-
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. 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: RnRS Triple: [R^nRS, hasAbbreviation, RnRS]
Generated description
RnRS is the standard specification document series that formally defines the Scheme programming language.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: RnRS Target entity description: RnRS is the standard specification document series that formally defines the Scheme programming language.
-
A.
R6RS
R6RS is the sixth revision of the Scheme programming language standard, defining its core language features, libraries, and semantics.
-
B.
R4RS
R4RS is the fourth revised report on the Scheme programming language standard, defining its core syntax, semantics, and standard procedures.
-
C.
R7RS (small) (partial)
R7RS (small) (partial) is a subset of the Revised⁷ Report on the Algorithmic Language Scheme standard that defines a core, lightweight version of the Scheme programming language.
-
D.
Scheme R5RS
Scheme R5RS is the fifth revised report of the Scheme programming language standard, defining its core syntax, semantics, and standard libraries.
-
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. 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b46e8ac8190bfa8c611ffcba822 |
completed | April 29, 2026, 1:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b0c74318081909fe8f6b794b6ce83 |
completed | May 18, 2026, 12:56 p.m. |
| NEDg | Description generation | batch_6a0b0d6d5b0c8190a96b3d556ccf5aee |
completed | May 18, 2026, 1 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b0e2082a88190b0c203cb933276dd |
completed | May 18, 2026, 1:03 p.m. |
Created at: April 16, 2026, 8:47 p.m.