Triple
T8083755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borda count |
E188679
|
entity |
| Predicate | typicalScoringVector |
P16725
|
FINISHED |
| Object | for n candidates, top rank gets n-1 points, next gets n-2, down to 0 |
—
|
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: for n candidates, top rank gets n-1 points, next gets n-2, down to 0 | Statement: [Borda count, typicalScoringVector, for n candidates, top rank gets n-1 points, next gets n-2, down to 0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalScoringVector Context triple: [Borda count, typicalScoringVector, for n candidates, top rank gets n-1 points, next gets n-2, down to 0]
-
A.
scoringType
Indicates the method or criteria by which performance, outcomes, or results are evaluated and assigned a score in a given context.
-
B.
scoring
Indicates the act of achieving points or a measurable result, typically by successfully completing an action that contributes to a score or outcome.
-
C.
scoringRecord
Indicates that there exists a record documenting a scoring event or outcome associated with the given entities.
-
D.
scoreScale
chosen
Indicates the scale or range on which a score or rating is expressed or measured.
-
E.
scoringUnit
Indicates that one entity functions as a unit or component responsible for scoring or assigning scores to another entity.
- 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_69ca82b662e88190b9323daab8c28a21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb415e61ac81909e924aea69a7ff77 |
completed | March 31, 2026, 3:37 a.m. |
| PD | Predicate disambiguation | batch_69cb049f1614819087360d1a4c6f0faa |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:29 p.m.