Triple
T8808194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsakonians |
E209586
|
entity |
| Predicate | languageUseTrend |
P84717
|
FINISHED |
| Object | declining |
—
|
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: declining | Statement: [Tsakonians, languageUseTrend, declining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageUseTrend Context triple: [Tsakonians, languageUseTrend, declining]
-
A.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
B.
languageOfMostTweets
Indicates the primary language in which the majority of a user's tweets are written.
-
C.
linguisticUsage
Indicates how a linguistic form, expression, or construction is used in language, such as its typical context, function, or register.
-
D.
languageUsedAs
Indicates that one language is employed in a specific role, function, or context relative to another entity or situation.
-
E.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
- 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_69ca8363f3308190a47e3f1ebd51f613 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fd4cbec8190a929d4e60da8ad65 |
completed | March 31, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5cff3608819081d2d7e5c16d44b7 |
completed | March 31, 2026, 11:47 p.m. |
Created at: March 30, 2026, 6:45 p.m.