Triple
T6696459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nganasan language |
E152761
|
entity |
| Predicate | hasAspectualDistinctions |
P27456
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Nganasan language, hasAspectualDistinctions, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAspectualDistinctions Context triple: [Nganasan language, hasAspectualDistinctions, yes]
-
A.
hasTenseAspect
Indicates that a verb or clause is associated with a specific grammatical tense and aspect configuration.
-
B.
hasVerbAspect
chosen
Indicates that a verb or verbal expression is associated with a particular grammatical aspect (such as perfective, imperfective, or progressive) describing the temporal structure of the action or state.
-
C.
hasTenseAspectSystem
Indicates that a language or clause employs a particular system for expressing tense and aspect distinctions.
-
D.
hasDefinitenessDistinction
Indicates that a language or system grammatically distinguishes between definite and indefinite (or otherwise specified) reference in its expressions.
-
E.
hasLanguageAspect
Indicates that an entity is associated with a particular linguistic aspect, such as tense, mood, or grammatical feature, in relation to a language.
- 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_69c6880687b08190805278b504d1c92c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6b5ed99e48190970805225458ce82 |
completed | March 27, 2026, 4:53 p.m. |
| PD | Predicate disambiguation | batch_69c6ad0e1d348190af1762ea1951038e |
completed | March 27, 2026, 4:15 p.m. |
Created at: March 27, 2026, 2:05 p.m.