Triple
T16471661
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Georgian |
E400076
|
entity |
| Predicate | hasVerbAgreement |
P50348
|
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: [Old Georgian, hasVerbAgreement, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVerbAgreement Context triple: [Old Georgian, hasVerbAgreement, yes]
-
A.
hasVerbAspect
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.
-
B.
hasPersonMarkingOnVerb
chosen
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.
-
C.
hasSubjectPronouns
Indicates that an entity is associated with one or more pronouns that function as its grammatical subject in sentences.
-
D.
hasTense
Indicates that an action, event, or state is associated with a specific grammatical tense (such as past, present, or future).
-
E.
hasNoun
Indicates that an entity possesses or is associated with a specific noun as an attribute, label, or grammatical component.
- 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_69d87f2dac988190b74d6e185fa88ba4 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32dd19df881909e4562a5e8473338 |
completed | April 18, 2026, 7:08 a.m. |
| PD | Predicate disambiguation | batch_69e22706b0588190a48a951c5211a617 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:11 a.m.