Triple
T2206612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BASIC |
E50813
|
entity |
| Predicate | influencedBy |
P9
|
FINISHED |
| Object | Fortran |
E59594
|
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: Fortran | Statement: [BASIC, influencedBy, Fortran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fortran Context triple: [BASIC, influencedBy, Fortran]
-
A.
Fortran
chosen
Fortran is a high-level programming language, particularly strong in numerical and scientific computing, widely used for engineering, physics, and high-performance applications.
-
B.
Algol 68C
Algol 68C is a compiler implementation of the Algol 68 programming language, designed to translate its advanced structured constructs into executable machine code.
-
C.
Algol 68S
Algol 68S is a simplified subset of the Algol 68 programming language designed to make the language easier to implement and use.
-
D.
Algol 68
Algol 68 is a high-level, structured programming language from the ALGOL family, notable for its orthogonal design and influence on many later languages.
-
E.
Algol 68R
Algol 68R is a revised, more practical and implementable version of the Algol 68 programming language, created to simplify and clarify the original language’s complex design.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfca300c81908b33debafa77d152 |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae654b89b081908f8c8b9bfc0b6579 |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:46 p.m.