Triple
T38157388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | მიხეილ სააკაშვილი |
E952922
|
entity |
| Predicate | ეკავა თანამდებობა |
P190155
|
FINISHED |
| Object | საქართველოს იუსტიციის მინისტრი |
—
|
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: საქართველოს იუსტიციის მინისტრი | Statement: [მიხეილ სააკაშვილი, ეკავა თანამდებობა, საქართველოს იუსტიციის მინისტრი]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ეკავა თანამდებობა Context triple: [მიხეილ სააკაშვილი, ეკავა თანამდებობა, საქართველოს იუსტიციის მინისტრი]
-
A.
ენები
Indicates a relationship where one entity is associated with, uses, or is characterized by certain languages.
-
B.
ეროვნება
Indicates a relationship where an entity has or is associated with a particular nationality.
-
C.
結成の場
Indicates the context, situation, or occasion in which a group, organization, or partnership is formed or established.
-
D.
ოჯახი
Indicates a familial relationship or connection between people, such as being members of the same family unit.
-
E.
قبلة
Indicates a kissing action or gesture occurring between entities.
- 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_69f76f0b93c48190a117319ab3a9f282 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcb089a8f881909aa9e722babd43f7 |
completed | May 7, 2026, 3:32 p.m. |
| PD | Predicate disambiguation | batch_69fc45666c5c8190913bd632ac0e5b84 |
completed | May 7, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69fcb088aac481909db90804faff315f |
completed | May 7, 2026, 3:32 p.m. |
Created at: May 3, 2026, 4:21 p.m.