Triple
T2629276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fortran |
E59594
|
entity |
| Predicate | standardizedBy |
P1371
|
FINISHED |
| Object | ANSI |
E6170
|
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: ANSI | Statement: [Fortran, standardizedBy, ANSI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ANSI Context triple: [Fortran, standardizedBy, ANSI]
-
A.
ANSI
chosen
ANSI is a private non-profit organization that oversees the development and coordination of voluntary consensus standards for products, services, processes, and systems in the United States.
-
B.
ISO 646
ISO 646 is an international standard for 7-bit character encodings that defines a set of basic Latin characters and allows national variants, serving as a foundation for many early computer character sets.
-
C.
ANSI X3.159-1989
ANSI X3.159-1989 is the original American national standard that formally defined the C programming language.
-
D.
ASCII
ASCII is a widely used character encoding standard that represents text in computers and other devices using 7-bit numerical codes for letters, digits, punctuation, and control characters.
-
E.
ASCI
ASCI is a prestigious U.S. honor society of physician-scientists dedicated to advancing clinical and translational research.
- 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_69ab4ac8596c8190b34997e73d9e991c |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd8c452508190b02e1630d725497a |
completed | March 7, 2026, 7:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af90a44a348190b8b49b37418dd94b |
completed | March 10, 2026, 3:31 a.m. |
Created at: March 6, 2026, 9:50 p.m.