Triple
T17148059
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NET |
E416145
|
entity |
| Predicate | capValueForScoringMargin |
P126296
|
FINISHED |
| Object | +10 points per game |
—
|
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: +10 points per game | Statement: [NET, capValueForScoringMargin, +10 points per game]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: capValueForScoringMargin Context triple: [NET, capValueForScoringMargin, +10 points per game]
-
A.
scoreMargin
Indicates the difference in score between two competitors or sides in a contest or game.
-
B.
finalRoundMargin
Indicates the point or score difference between competitors in the final round of a contest or competition.
-
C.
marginOf
Indicates the difference or buffer between two related quantities, values, or boundaries, often expressing how much one exceeds or falls short of another.
-
D.
scoringUnit
Indicates that one entity functions as a unit or component responsible for scoring or assigning scores to another entity.
-
E.
targetScoreRule
Indicates the rule or criteria used to determine the target score to be achieved or applied in a given context.
- 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_69d886d279c081909f8ff1f743ddeb69 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f404f0e88190b7ac9ac523fdc7da |
completed | April 18, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69e3830d2a90819092386717dc56f0e8 |
completed | April 18, 2026, 1:11 p.m. |
| PDg | Predicate description generation | batch_69e3873f62108190966c4e741ebd548d |
completed | April 18, 2026, 1:29 p.m. |
Created at: April 10, 2026, 5:36 a.m.