Triple
T37836662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ლევან კობიაშვილი |
E943355
|
entity |
| Predicate | საკლუბო_კარიერა_დაიწყო |
P78185
|
FINISHED |
| Object | მერანი თბილისი |
—
|
NE NERFINISHED |
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: მერანი თბილისი | Statement: [ლევან კობიაშვილი, საკლუბო_კარიერა_დაიწყო, მერანი თბილისი]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: საკლუბო_კარიერა_დაიწყო Context triple: [ლევან კობიაშვილი, საკლუბო_კარიერა_დაიწყო, მერანი თბილისი]
-
A.
clubCareerStart
chosen
Indicates the point in time when an entity begins its professional or organized club-level career.
-
B.
beganBroadcastingCareer
Indicates that an entity started or initiated its professional career in broadcasting at a particular time or context.
-
C.
startedCupCareer
Indicates that an entity began its participation or professional involvement in a cup-based competition or series at a specified time or event.
-
D.
launchedCareerOf
Indicates that one entity’s actions, support, or involvement initiated or significantly advanced another entity’s professional career.
-
E.
associatedClubBeganPlay
Indicates that the related club began its competitive play or official participation at the specified time.
- 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_69f76eeb0f7081908d6d3adbc469889c |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbbae559a8819086ef839973f8d9b2 |
completed | May 6, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69fbb1440fa08190abf25ba684f75b6e |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:19 p.m.