Triple
T8565834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nubian languages |
E202798
|
entity |
| Predicate | haveCaseMarking |
P12254
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Nubian languages, haveCaseMarking, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveCaseMarking Context triple: [Nubian languages, haveCaseMarking, true]
-
A.
hasCaseMarking
chosen
Indicates that a linguistic element (such as a noun or pronoun) bears a specific grammatical case marking that signals its syntactic or semantic role in a clause.
-
B.
hasCaseForms
Indicates that an entity possesses multiple grammatical case variants or inflected forms associated with it.
-
C.
hasPersonMarkingOnVerb
Indicates that the verb carries explicit grammatical marking that identifies or agrees with the person (e.g., first, second, third person) of its subject or argument.
-
D.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
E.
usesToneMarks
Indicates that one entity applies or includes diacritical tone marks in the representation or transcription of another entity (such as text, language, or symbols).
- 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_69ca8327b0a881908606ff860713964d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe9d2331881909d92ddde90f580e9 |
completed | March 31, 2026, 3:35 p.m. |
| PD | Predicate disambiguation | batch_69cbd11856048190a1ce4b83a38f6965 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:20 p.m.