Triple
T3018760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NISA |
E82402
|
entity |
| Predicate | sanctioningLevel |
P45060
|
FINISHED |
| Object | Division III |
—
|
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: Division III | Statement: [NISA, sanctioningLevel, Division III]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sanctioningLevel Context triple: [NISA, sanctioningLevel, Division III]
-
A.
sanction
Indicates the imposition of an official penalty or restrictive measure by an authority in response to certain actions or behaviors.
-
B.
typeOfSanction
Indicates the specific category or kind of sanction that is applied in a given situation.
-
C.
canBeSanctionedFor
Indicates that an entity is subject to penalties or disciplinary measures as a consequence of a specified action, behavior, or condition.
-
D.
receivedSanctionFrom
Indicates that one entity has been subjected to a formal penalty, restriction, or disciplinary measure imposed by another entity.
-
E.
sponsorLevel
Indicates the degree or tier of sponsorship that one entity provides to another.
- 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_69ad8b1fb34081908c1b873e2b7273e1 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a927b608190ba1392498507b237 |
completed | March 8, 2026, 3:49 p.m. |
| PD | Predicate disambiguation | batch_69ad961a97188190809dc73430a8eda8 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97ba55dc8190b6dddddfb751cf64 |
completed | March 8, 2026, 3:37 p.m. |
Created at: March 8, 2026, 3 p.m.