Triple
T6672573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kawa |
E151765
|
entity |
| Predicate | conformsTo |
P3994
|
FINISHED |
| Object | R6RS (partially) |
E573197
|
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: R6RS (partially) | Statement: [Kawa, conformsTo, R6RS (partially)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: R6RS (partially) Context triple: [Kawa, conformsTo, R6RS (partially)]
-
A.
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.
-
B.
R6RS
chosen
R6RS is the sixth revision of the Scheme programming language standard, defining its core language features, libraries, and semantics.
-
C.
R4RS
R4RS is the fourth revised report on the Scheme programming language standard, defining its core syntax, semantics, and standard procedures.
-
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.
R6RS I/O system
The R6RS I/O system is the standardized input/output framework defined by the Revised⁶ Report on the Algorithmic Language Scheme, providing a rich, port-based model for text and binary data handling.
- 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_69c687f71fc081909dbd45d6377f6045 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b0cb78b08190923685712cbba5d8 |
completed | March 27, 2026, 4:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6f79f1718819098d8a6d08bf7f919 |
completed | March 27, 2026, 9:33 p.m. |
Created at: March 27, 2026, 2:03 p.m.