Triple
T26317716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | nroff |
E662017
|
entity |
| Predicate | usesMacroLanguage |
P166612
|
FINISHED |
| Object | roff macro language |
—
|
LITERAL 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: roff macro language | Statement: [nroff, usesMacroLanguage, roff macro language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMacroLanguage Context triple: [nroff, usesMacroLanguage, roff macro language]
-
A.
hasMacroLanguage
Indicates that one language functions as a macrolanguage encompassing or grouping together multiple closely related individual languages or varieties.
-
B.
hasMacrolanguage
Indicates that a language is part of, or grouped under, a broader macrolanguage that encompasses multiple closely related language varieties.
-
C.
macrolanguageWith
Indicates that one language is classified as a macrolanguage that encompasses or groups together another, more specific language variety.
-
D.
MCUUsesLanguage
Indicates that a particular MCU (microcontroller unit) is programmed or operated using a specified programming language.
-
E.
macrolanguage
Indicates that a language is classified as a macrolanguage encompassing multiple closely related individual languages or varieties.
- F. None of above. chosen
Provenance (4 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_69ee812e73048190aae587f1d51e5a06 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f662a29b3881909957a7e3b986653c |
completed | May 2, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69f660eea4648190b0d5e24293607813 |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f661b47d088190934f63884a203261 |
completed | May 2, 2026, 8:42 p.m. |
Created at: April 26, 2026, 10:26 p.m.