Triple
T5847896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neil Bonnett |
E129756
|
entity |
| Predicate | bestCupSeasonPointsPosition |
P18481
|
FINISHED |
| Object | 4th |
—
|
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: 4th | Statement: [Neil Bonnett, bestCupSeasonPointsPosition, 4th]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestCupSeasonPointsPosition Context triple: [Neil Bonnett, bestCupSeasonPointsPosition, 4th]
-
A.
bestLeagueFinish
chosen
Indicates the highest final position or ranking an entity has ever achieved in a particular league or competition.
-
B.
topScorerPoints
Indicates the number of points scored by the top-scoring entity in a given context or event.
-
C.
topScorer
Indicates that the subject is the individual with the highest score among a specified group or in a particular context.
-
D.
seasonPointsRecordSet
Indicates that a new record for total points scored in a season has been achieved or established.
-
E.
scored100PointSeason
Indicates that an entity (typically an athlete) completed a season in which they scored at least 100 points.
- 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_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. |
Created at: March 22, 2026, 3:55 p.m.