Triple
T5847897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neil Bonnett |
E129756
|
entity |
| Predicate | bestCupSeasonPointsYear |
P66694
|
FINISHED |
| Object | 1985 |
—
|
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: 1985 | Statement: [Neil Bonnett, bestCupSeasonPointsYear, 1985]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestCupSeasonPointsYear Context triple: [Neil Bonnett, bestCupSeasonPointsYear, 1985]
-
A.
bestSeasonResultYear
Indicates the year in which an entity achieved its best season result.
-
B.
perfectSeasonYear
Indicates the year in which an entity (such as a team or individual) completed a perfect, undefeated season.
-
C.
scored100PointSeason
Indicates that an entity (typically an athlete) completed a season in which they scored at least 100 points.
-
D.
bestRegionalResultYear
Indicates the year in which an entity achieved its best performance or highest result within a specific region.
-
E.
NBASeasonScoringLeader
Indicates that the subject was the player who scored the most total points in a given NBA season.
- 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_69c0084bd31c8190a796bb6284845e83 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c03c9239e08190bff7ef2bd6d21ae0 |
completed | March 22, 2026, 7:01 p.m. |
| PD | Predicate disambiguation | batch_69c0334412388190bc594794ec5754f9 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c03c8d579081909d7b97fc9014b5d7 |
completed | March 22, 2026, 7:01 p.m. |
Created at: March 22, 2026, 3:55 p.m.