Triple
T5008800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lezgian |
E112561
|
entity |
| Predicate | hasCaseNumber |
P1437
|
FINISHED |
| Object | large number of grammatical cases |
—
|
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: large number of grammatical cases | Statement: [Lezgian, hasCaseNumber, large number of grammatical cases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCaseNumber Context triple: [Lezgian, hasCaseNumber, large number of grammatical cases]
-
A.
hasCase
Indicates that one entity is involved in, associated with, or characterized by a particular case, instance, or occurrence represented by another entity.
-
B.
hasNumberOfCasesApprox
chosen
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
C.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
D.
hasTypeOfCase
Indicates that an entity is associated with or classified under a particular type or category of case.
-
E.
clauseNumber
Indicates the specific numbered position or identifier assigned to a clause within a larger document or agreement.
- 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_69bd4433d0b08190877e83959ef40d81 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd730a7590819088ab8d49c5c88c2f |
completed | March 20, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69bd714cbc448190aa53a8a83d768b64 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:35 p.m.