Triple
T2314122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SETL |
E51023
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | SETL2 |
E51023
|
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: SETL2 | Statement: [SETL, influenced, SETL2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SETL2 Context triple: [SETL, influenced, SETL2]
-
A.
SETL
chosen
SETL is a high-level programming language developed in the late 1960s that is notable for its powerful set-theoretic abstractions and influence on later language design.
-
B.
Modula-2
Modula-2 is a systems programming language designed by Niklaus Wirth that extends Pascal with modules, concurrency features, and low-level facilities for structured, efficient software development.
-
C.
NTL
NTL was a major UK cable television and telecommunications company that became part of Virgin Media following a series of mergers and rebrandings.
-
D.
Oberon-2
Oberon-2 is an object-oriented, statically typed programming language that extends Niklaus Wirth’s Oberon with features like type-bound procedures and read-only export while preserving simplicity and efficiency.
-
E.
Algol 68S
Algol 68S is a simplified subset of the Algol 68 programming language designed to make the language easier to implement and use.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc61c1ef08190911d5f58c2e91189 |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae895f5420819087b403e9772dce9a |
completed | March 9, 2026, 8:48 a.m. |
Created at: March 4, 2026, 7:49 p.m.