Triple
T29404586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GObject |
E745748
|
entity |
| Predicate | hasMacro |
P194366
|
FINISHED |
| Object | G_OBJECT_CLASS |
—
|
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: G_OBJECT_CLASS | Statement: [GObject, hasMacro, G_OBJECT_CLASS]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMacro Context triple: [GObject, hasMacro, G_OBJECT_CLASS]
-
A.
hasMacroLanguage
Indicates that one language functions as a macrolanguage encompassing or grouping together multiple closely related individual languages or varieties.
-
B.
usesMacroLanguage
Indicates that one entity communicates or operates using a broader macro language that encompasses or organizes other related languages or language varieties.
-
C.
hasMacroArea
Indicates that one entity belongs to, or is located within, a broader geographic or conceptual macro-area represented by another entity.
-
D.
definesMacro
chosen
Indicates that one entity specifies or declares a macro that can be used or expanded by another entity.
-
E.
hasProposedMacroFamily
Indicates that one linguistic entity has been proposed as belonging to the same larger, hypothetical macro-family as another linguistic entity.
- F. None of above.
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_69f0a79eb7d081908c67197a5f347e68 |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69ff409ff5548190849c2d50e99bd807 |
completed | May 9, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69ff401a5e188190a72f945e910b4a6c |
completed | May 9, 2026, 2:09 p.m. |
Created at: April 28, 2026, 2:53 p.m.